Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 263

Search results for: algorithmic trading

263 A Prediction Model Using the Price Cyclicality Function Optimized for Algorithmic Trading in Financial Market

Authors: Cristian Păuna

Abstract:

After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, price prediction

Procedia PDF Downloads 84
262 A Multi-Dimensional Neural Network Using the Fisher Transform to Predict the Price Evolution for Algorithmic Trading in Financial Markets

Authors: Cristian Pauna

Abstract:

Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

Procedia PDF Downloads 82
261 A Data Science Pipeline for Algorithmic Trading: A Comparative Study in Applications to Finance and Cryptoeconomics

Authors: Luyao Zhang, Tianyu Wu, Jiayi Li, Carlos-Gustavo Salas-Flores, Saad Lahrichi

Abstract:

Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications.

Keywords: algorithmic trading, AI for finance, fintech, machine learning, moving average crossover, volume weighted average price, sentiment analysis, statistical arbitrage, pair trading, object-oriented programming, python3

Procedia PDF Downloads 45
260 Hidden Markov Model for Financial Limit Order Book and Its Application to Algorithmic Trading Strategy

Authors: Sriram Kashyap Prasad, Ionut Florescu

Abstract:

This study models the intraday asset prices as driven by Markov process. This work identifies the latent states of the Hidden Markov model, using limit order book data (trades and quotes) to continuously estimate the states throughout the day. This work builds a trading strategy using estimated states to generate signals. The strategy utilizes current state to recalibrate buy/ sell levels and the transition between states to trigger stop-loss when adverse price movements occur. The proposed trading strategy is tested on the Stevens High Frequency Trading (SHIFT) platform. SHIFT is a highly realistic market simulator with functionalities for creating an artificial market simulation by deploying agents, trading strategies, distributing initial wealth, etc. In the implementation several assets on the NASDAQ exchange are used for testing. In comparison to a strategy with static buy/ sell levels, this study shows that the number of limit orders that get matched and executed can be increased. Executing limit orders earns rebates on NASDAQ. The system can capture jumps in the limit order book prices, provide dynamic buy/sell levels and trigger stop loss signals to improve the PnL (Profit and Loss) performance of the strategy.

Keywords: algorithmic trading, Hidden Markov model, high frequency trading, limit order book learning

Procedia PDF Downloads 58
259 Business and Psychological Principles Integrated into Automated Capital Investment Systems through Mathematical Algorithms

Authors: Cristian Pauna

Abstract:

With few steps away from the 2020, investments in financial markets is a common activity nowadays. In the electronic trading environment, the automated investment software has become a major part in the business intelligence system of any modern financial company. The investment decisions are assisted and/or made automatically by computers using mathematical algorithms today. The complexity of these algorithms requires computer assistance in the investment process. This paper will present several investment strategies that can be automated with algorithmic trading for Deutscher Aktienindex DAX30. It was found that, based on several price action mathematical models used for high-frequency trading some investment strategies can be optimized and improved for automated investments with good results. This paper will present the way to automate these investment decisions. Automated signals will be built using all of these strategies. Three major types of investment strategies were found in this study. The types are separated by the target length and by the exit strategy used. The exit decisions will be also automated and the paper will present the specificity for each investment type. A comparative study will be also included in this paper in order to reveal the differences between strategies. Based on these results, the profit and the capital exposure will be compared and analyzed in order to qualify the investment methodologies presented and to compare them with any other investment system. As conclusion, some major investment strategies will be revealed and compared in order to be considered for inclusion in any automated investment system.

Keywords: Algorithmic trading, automated investment systems, limit conditions, trading principles, trading strategies

Procedia PDF Downloads 97
258 The Carbon Trading Price and Trading Volume Forecast in Shanghai City by BP Neural Network

Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

Procedia PDF Downloads 223
257 Price Prediction Line, Investment Signals and Limit Conditions Applied for the German Financial Market

Authors: Cristian Păuna

Abstract:

In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

Procedia PDF Downloads 63
256 Risk Factors’ Analysis on Shanghai Carbon Trading

Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

Abstract:

First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model

Procedia PDF Downloads 308
255 Parabolic Impact Law of High Frequency Exchanges on Price Formation in Commodities Market

Authors: L. Maiza, A. Cantagrel, M. Forestier, G. Laucoin, T. Regali

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Evaluation of High Frequency Trading (HFT) impact on financial markets is very important for traders who use market analysis to detect winning transaction opportunity. Analysis of HFT data on tobacco commodity market is discussed here and interesting linear relationship has been shown between trading frequency and difference between averaged trading prices above and below considered trading frequency. This may open new perspectives on markets data understanding and could provide possible interpretation of Adam Smith invisible hand.

Keywords: financial market, high frequency trading, analysis, impacts, Adam Smith invisible hand

Procedia PDF Downloads 246
254 Application of the Discrete-Event Simulation When Optimizing of Business Processes in Trading Companies

Authors: Maxat Bokambayev, Bella Tussupova, Aisha Mamyrova, Erlan Izbasarov

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Optimization of business processes in trading companies is reviewed in the report. There is the presentation of the “Wholesale Customer Order Handling Process” business process model applicable for small and medium businesses. It is proposed to apply the algorithm for automation of the customer order processing which will significantly reduce labor costs and time expenditures and increase the profitability of companies. An optimized business process is an element of the information system of accounting of spare parts trading network activity. The considered algorithm may find application in the trading industry as well.

Keywords: business processes, discrete-event simulation, management, trading industry

Procedia PDF Downloads 252
253 Options Trading and Crash Risk

Authors: Cameron Truong, Mikhail Bhatia, Yangyang Chen, Viet Nga Cao

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Using a sample of U.S. firms between 1996 and 2011, this paper documents a positive association between options trading volume and future stock price crash risk. This relation is evidently more pronounced among firms with higher information asymmetry, business uncertainty, and short-sale constraints. In a dichotomous cross-sectional setting, we also document that firms with options trading have higher future crash risk than firms without options trading. We further show in a difference-in-difference analysis that firms experience an increase in crash risk immediately after the listing of options. The results suggest that options traders are able of identifying bad news hoarding by management and choose to trade in a liquid options market in anticipation of future crashes.

Keywords: bad news hoarding, cross-sectional setting, options trading, stock price crash

Procedia PDF Downloads 371
252 Enhancing a Recidivism Prediction Tool with Machine Learning: Effectiveness and Algorithmic Fairness

Authors: Marzieh Karimihaghighi, Carlos Castillo

Abstract:

This work studies how Machine Learning (ML) may be used to increase the effectiveness of a criminal recidivism risk assessment tool, RisCanvi. The two key dimensions of this analysis are predictive accuracy and algorithmic fairness. ML-based prediction models obtained in this study are more accurate at predicting criminal recidivism than the manually-created formula used in RisCanvi, achieving an AUC of 0.76 and 0.73 in predicting violent and general recidivism respectively. However, the improvements are small, and it is noticed that algorithmic discrimination can easily be introduced between groups such as national vs foreigner, or young vs old. It is described how effectiveness and algorithmic fairness objectives can be balanced, applying a method in which a single error disparity in terms of generalized false positive rate is minimized, while calibration is maintained across groups. Obtained results show that this bias mitigation procedure can substantially reduce generalized false positive rate disparities across multiple groups. Based on these results, it is proposed that ML-based criminal recidivism risk prediction should not be introduced without applying algorithmic bias mitigation procedures.

Keywords: algorithmic fairness, criminal risk assessment, equalized odds, recidivism

Procedia PDF Downloads 65
251 The Impact of Insider Trading on Open Market Share Repurchase: A Study in Indian Context

Authors: Sarthak Kumar Jena, Chandra Sekhar Mishra, Prabina Rajib

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Purpose: This paper aims to derive undervaluation signal from the insiders trading of Indian companies where the ownership is complex and concentrated, investors protection is weak, and the insider rules and regulations are not stringent like developed country. This study examines the relationship between insider trading with short term and long term abnormal return. The study also examines the relationship between insider trading and the actual share repurchase by the firm. Methodology: A sample of 78 companies over the period 2008-2013 are analyzed in the study due to not availability of insider data in Indian context. For preliminary analysis T-test and Wilcoxon rank sum test is used to find the difference between the insider trading before and after the share repurchase announcement. Tobit model is used to find out whether insider trading influence shares repurchase decisions or not. Return on the basis of market model and buy hold are calculated in the previous year and the following year of share repurchase announcement. Findings: The paper finds that insider trading around share repurchase is more than control firms and there is positive and significant difference in insider buying between the previous year of share buyback announcement and the following year of buyback announcement. Insider buying before share repurchase announcement has a positive influence on share repurchase decisions. We find insider buying has a positive and significant relationship with announcement return, whereas insider selling has a negative significant relationship with announcement return. Actual share repurchase and program completion also depend on insider trading before share repurchase. Research limitation: The study is constrained by the small sample size, so the results should be viewed by keeping this limitation in mind. Originality: The paper is to our best knowledge the first study based on Indian context to extend the insider trading literature to share repurchase event and examine insider trading to find out undervaluation signal associated with insider buying.

Keywords: insider trading, buyback, open market share repurchase, signalling

Procedia PDF Downloads 103
250 A Stochastic Volatility Model for Optimal Market-Making

Authors: Zubier Arfan, Paul Johnson

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The electronification of financial markets and the rise of algorithmic trading has sparked a lot of interest from the mathematical community, for the market making-problem in particular. The research presented in this short paper solves the classic stochastic control problem in order to derive the strategy for a market-maker. It also shows how to calibrate and simulate the strategy with real limit order book data for back-testing. The ambiguity of limit-order priority in back-testing is dealt with by considering optimistic and pessimistic priority scenarios. The model, although it does outperform a naive strategy, assumes constant volatility, therefore, is not best suited to the LOB data. The Heston model is introduced to describe the price and variance process of the asset. The Trader's constant absolute risk aversion utility function is optimised by numerically solving a 3-dimensional Hamilton-Jacobi-Bellman partial differential equation to find the optimal limit order quotes. The results show that the stochastic volatility market-making model is more suitable for a risk-averse trader and is also less sensitive to calibration error than the constant volatility model.

Keywords: market-making, market-microsctrucure, stochastic volatility, quantitative trading

Procedia PDF Downloads 42
249 A Hybrid Expert System for Generating Stock Trading Signals

Authors: Hosein Hamisheh Bahar, Mohammad Hossein Fazel Zarandi, Akbar Esfahanipour

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In this paper, a hybrid expert system is developed by using fuzzy genetic network programming with reinforcement learning (GNP-RL). In this system, the frame-based structure of the system uses the trading rules extracted by GNP. These rules are extracted by using technical indices of the stock prices in the training time period. For developing this system, we applied fuzzy node transition and decision making in both processing and judgment nodes of GNP-RL. Consequently, using these method not only did increase the accuracy of node transition and decision making in GNP's nodes, but also extended the GNP's binary signals to ternary trading signals. In the other words, in our proposed Fuzzy GNP-RL model, a No Trade signal is added to conventional Buy or Sell signals. Finally, the obtained rules are used in a frame-based system implemented in Kappa-PC software. This developed trading system has been used to generate trading signals for ten companies listed in Tehran Stock Exchange (TSE). The simulation results in the testing time period shows that the developed system has more favorable performance in comparison with the Buy and Hold strategy.

Keywords: fuzzy genetic network programming, hybrid expert system, technical trading signal, Tehran stock exchange

Procedia PDF Downloads 237
248 Societal Impacts of Algorithmic Recommendation System: Economy, International Relations, Political Ideologies, and Education

Authors: Maggie Shen

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Ever since the late 20th century, business giants have been competing to provide better experiences for their users. One way they strive to do so is through more efficiently connecting users with their goals, with recommendation systems that filter out unnecessary or less relevant information. Today’s top online platforms such as Amazon, Netflix, Airbnb, Tiktok, Facebook, and Google all utilize algorithmic recommender systems for different purposes—Product recommendation, movie recommendation, travel recommendation, relationship recommendation, etc. However, while bringing unprecedented convenience and efficiency, the prevalence of algorithmic recommendation systems also influences society in many ways. In using a variety of primary, secondary, and social media sources, this paper explores the impacts of algorithms, particularly algorithmic recommender systems, on different sectors of society. Four fields of interest will be specifically addressed in this paper: economy, international relations, political ideologies, and education.

Keywords: algorithms, economy, international relations, political ideologies, education

Procedia PDF Downloads 86
247 Fragmentation of The Multilateral Trading System: The Impact of Regionalism on WTO Law

Authors: Musa Njabulo Shongwe

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The multilateral trading system is facing a great danger of fragmentation. Its modus operandi, multilateralism, is increasingly becoming clogged by trade barriers created by the proliferation of preferential regional trading blocs. The paper explores the fragmentation of the multilateral trade regulation system (WTO law) by analysing whether and to what extent Regional Trade Agreements (RTAs) have conflicted with the Multilateral Trading System. The paper examines the effects of RTA dominance in view of the WTO's quest for trade liberalization. This is an important inquiry because the proliferation of RTAs implies the erosion of the WTO law’s core principle of non-discrimination. The paper further explores how the proliferation of RTAs has endangered the coherence of the multilateral trading system. The study is carried out with the initial assumption that RTAs could be complementary and coherent with WTO law, and thus facilitate international trade and enhance development prospects. There is evidence that is tested by this study which suggests that RTAs can be divergent and hence undermine the WTO multilateral rules of regulating international trade. The paper finally recommends legal tools of regulating and managing the WTO-RTA interface, as well as other legal means of ensuring a harmonious existence between the WTO and regional trade arrangements.

Keywords: fragmentation of international trade law, regionalism, regional trade agreements, WTO law

Procedia PDF Downloads 290
246 Algorithmic Skills Transferred from Secondary CSI Studies into Tertiary Education

Authors: Piroska Biró, Mária Csernoch, János Máth, Kálmán Abari

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Testing the first year students of Informatics at the University of Debrecen revealed that students start their tertiary studies in programming with a low level of programming knowledge and algorithmic skills. The possible reasons which lead the students to this very unfortunate result were examined. The results of the test were compared to the students’ results in the school leaving exams and to their self-assessment values. It was found that there is only a slight connection between the students’ results in the test and in the school leaving exams, especially at intermediate level. Beyond this, the school leaving exams do not seem to enable students to evaluate their own abilities.

Keywords: deep and surface approaches, metacognitive abilities, programming and algorithmic skills, school leaving exams, tracking code

Procedia PDF Downloads 310
245 ‘It Is a Class Thing’: Socio-Economic Factors Sustaining Illicit Trading in New Naira Notes in Ibadan, Nigeria

Authors: Frank C. Amaechi, Adeyinka A. Aderinto, Usman A. Ojedokun, Oludayo Tade

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Illicit trading in new naira notes has become a common practice in most communities in Nigeria despite the Central Bank Act’s in 2007 proscription of all forms of naira abuse. This study investigated the socio-economic factors sustaining illicit trading in new naira notes in Ibadan metropolis. The study was exploratory and cross-sectional in design. Neutralization theory was adopted as theoretical framework. Data were generated through the combination of in-depth interview and key informant interview methods. The purposive sampling technique was utilised to select five illicit traders of new naira notes, 32 patrons of the trade and six bank officials. Findings revealed that illicit trading in Nigeria’s national currency is flourishing because of the frequent demand for new naira notes that are not readily available in Nigerian banks. Also, the norm of cash spraying at social events is sustaining the illicit markets for new naira notes in Ibadan metropolis. In addition, a chain of network, comprising three principal actors, is behind the illegal business. A strict enforcement of the law banning cash spraying is advocated as a means of arresting this phenomenon.

Keywords: illicit trading, naira notes, national currency, Nigeria

Procedia PDF Downloads 219
244 Regional Trade Agreements versus the WTO: A Human Rights Perspective

Authors: Mohsen Qasemi

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In the international economic order multilateral trading system which established by General Agreement on Tariffs and Trade 1947 (GATT) was dominant until about two decades ago. Regional Trade Agreements (RTAs) have changed this order and become an important phenomenon. One of the main objectives of the World Trade Organization (WTO) as a central institution of multilateral trading system is raising standards of living. There are many scholars who suggest that WTO should take steps to protect human rights in its activities. Although it has always been opposing views who declare that since WTO has no explicit rule for human rights, it has no human rights related obligations. At the time that the WTO was established, member states began to join RTAs and since then, the escalating growth of these agreements and their effects on multilateral trading system has been controversial. There are some aspects of RTAs that have received too little attention from scholars. It is important to take a different view and evaluate the RTAs based on non-commercial aspects. The present paper seeks to answer this question: which system could be more useful in protecting human rights, RTAs or WTO?

Keywords: WTO, RTAs, human rights, multilateral trading system, non discrimination

Procedia PDF Downloads 278
243 Detecting Impact of Allowance Trading Behaviors on Distribution of NOx Emission Reductions under the Clean Air Interstate Rule

Authors: Yuanxiaoyue Yang

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Emissions trading, or ‘cap-and-trade', has been long promoted by economists as a more cost-effective pollution control approach than traditional performance standard approaches. While there is a large body of empirical evidence for the overall effectiveness of emissions trading, relatively little attention has been paid to other unintended consequences brought by emissions trading. One important consequence is that cap-and-trade could introduce the risk of creating high-level emission concentrations in areas where emitting facilities purchase a large number of emission allowances, which may cause an unequal distribution of environmental benefits. This study will contribute to the current environmental policy literature by linking trading activity with environmental injustice concerns and empirically analyzing the causal relationship between trading activity and emissions reduction under a cap-and-trade program for the first time. To investigate the potential environmental injustice concern in cap-and-trade, this paper uses a differences-in-differences (DID) with instrumental variable method to identify the causal effect of allowance trading behaviors on emission reduction levels under the clean air interstate rule (CAIR), a cap-and-trade program targeting on the power sector in the eastern US. The major data source is the facility-year level emissions and allowance transaction data collected from US EPA air market databases. While polluting facilities from CAIR are the treatment group under our DID identification, we use non-CAIR facilities from the Acid Rain Program - another NOx control program without a trading scheme – as the control group. To isolate the causal effects of trading behaviors on emissions reduction, we also use eligibility for CAIR participation as the instrumental variable. The DID results indicate that the CAIR program was able to reduce NOx emissions from affected facilities by about 10% more than facilities who did not participate in the CAIR program. Therefore, CAIR achieves excellent overall performance in emissions reduction. The IV regression results also indicate that compared with non-CAIR facilities, purchasing emission permits still decreases a CAIR participating facility’s emissions level significantly. This result implies that even buyers under the cap-and-trade program have achieved a great amount of emissions reduction. Therefore, we conclude little evidence of environmental injustice from the CAIR program.

Keywords: air pollution, cap-and-trade, emissions trading, environmental justice

Procedia PDF Downloads 27
242 Business Challenges and Opportunities of Mobile Applications for Equity Trading in India

Authors: Helee Dave

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Globalization has helped in the growth and change of the Indian economy to a great extent. The purchasing power of Indians has increased. IT Infrastructure has considerably improved in India. There is an increase in the usage of smartphones. The smartphones facilitate all sorts of work now a day, from getting groceries to planning a tour; it is just one click away. Similar is the case with equity trading. The traders in equity market can now deal with their stocks through mobile applications eliminating the middle man. The traders do not have an option but to open a dematerialization account with the banks which are compulsory enough irrespective of their mode of transaction that is online or offline. Considering that India is a young country having more than 50% of its population below the age of 25 and 65% of its population below the age of 35; this youth is comfortable with the usage of smartphones. The banking industry is also providing a virtual platform supporting equity market industry. Yet equity trading through online applications is at an infant stage. This paper primarily attempts to understand challenges and opportunities faced by equity trading through mobile apps in India.

Keywords: BPO, business process outsourcing, de-materialization account, equity, ITES, information technology enabled services

Procedia PDF Downloads 222
241 Reverse Logistics, Green Supply Chain, and Carbon Trading

Authors: Neha Asthana, Vishal Krishna Prasad

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Reverse logistics and green supply chain form an interconnected and interwoven network of parameters that contribute to enhancement and incremental exchange in the triple bottom line in the consistently changing and fragmenting markets of the globalizing markets of today. Reverse logistics not only contributes to completing the supply chain in a comprehensive and synchronized manner but also contributes to a significant degree in optimizing green supply chains through procedures such as recycling, refurbishing etc. contributing to waste reduction. Carbon trading, owing to its limitations in the global context and being in a nascent stage seeks plethora of research to determine its full application in synergy with reverse logistics and green supply chain.

Keywords: reverse logistics, carbon trading, carbon emissions, green supply chain

Procedia PDF Downloads 309
240 Reasons of Change in Security Prices and Price Volatility: An Analysis of the European Carbon Futures Market

Authors: Boulis M. Ibrahim, Iordanis A. Kalaitzoglou

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A micro structural pricing model is proposed in which price components account for learning by incorporating changing expectations of the trading intensity and the risk level of incoming trades. An analysis of European carbon futures transactions finds expected trading intensity to increase the information component and decrease the liquidity component of price changes, but at different rates. Among the results, the expected persistence in trading intensity explains the majority of the auto correlations in the level and the conditional volatility of price changes, helps predict hourly patterns in the bid–ask spread and differentiates between the impact of buy versus sell and continuing versus reversing trades.

Keywords: CO2 emission allowances, market microstructure, duration, price discovery

Procedia PDF Downloads 329
239 Computing Machinery and Legal Intelligence: Towards a Reflexive Model for Computer Automated Decision Support in Public Administration

Authors: Jacob Livingston Slosser, Naja Holten Moller, Thomas Troels Hildebrandt, Henrik Palmer Olsen

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In this paper, we propose a model for human-AI interaction in public administration that involves legal decision-making. Inspired by Alan Turing’s test for machine intelligence, we propose a way of institutionalizing a continuous working relationship between man and machine that aims at ensuring both good legal quality and higher efficiency in decision-making processes in public administration. We also suggest that our model enhances the legitimacy of using AI in public legal decision-making. We suggest that case loads in public administration could be divided between a manual and an automated decision track. The automated decision track will be an algorithmic recommender system trained on former cases. To avoid unwanted feedback loops and biases, part of the case load will be dealt with by both a human case worker and the automated recommender system. In those cases an experienced human case worker will have the role of an evaluator, choosing between the two decisions. This model will ensure that the algorithmic recommender system is not compromising the quality of the legal decision making in the institution. It also enhances the legitimacy of using algorithmic decision support because it provides justification for its use by being seen as superior to human decisions when the algorithmic recommendations are preferred by experienced case workers. The paper outlines in some detail the process through which such a model could be implemented. It also addresses the important issue that legal decision making is subject to legislative and judicial changes and that legal interpretation is context sensitive. Both of these issues requires continuous supervision and adjustments to algorithmic recommender systems when used for legal decision making purposes.

Keywords: administrative law, algorithmic decision-making, decision support, public law

Procedia PDF Downloads 30
238 Evaluation of Quasi-Newton Strategy for Algorithmic Acceleration

Authors: T. Martini, J. M. Martínez

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An algorithmic acceleration strategy based on quasi-Newton (or secant) methods is displayed for address the practical problem of accelerating the convergence of the Newton-Lagrange method in the case of convergence to critical multipliers. Since the Newton-Lagrange iteration converges locally at a linear rate, it is natural to conjecture that quasi-Newton methods based on the so called secant equation and some minimal variation principle, could converge superlinearly, thus restoring the convergence properties of Newton's method. This strategy can also be applied to accelerate the convergence of algorithms applied to fixed-points problems. Computational experience is reported illustrating the efficiency of this strategy to solve fixed-point problems with linear convergence rate.

Keywords: algorithmic acceleration, fixed-point problems, nonlinear programming, quasi-newton method

Procedia PDF Downloads 410
237 A Posteriori Trading-Inspired Model-Free Time Series Segmentation

Authors: Plessen Mogens Graf

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Within the context of multivariate time series segmentation, this paper proposes a method inspired by a posteriori optimal trading. After a normalization step, time series are treated channelwise as surrogate stock prices that can be traded optimally a posteriori in a virtual portfolio holding either stock or cash. Linear transaction costs are interpreted as hyperparameters for noise filtering. Trading signals, as well as trading signals obtained on the reversed time series, are used for unsupervised channelwise labeling before a consensus over all channels is reached that determines the final segmentation time instants. The method is model-free such that no model prescriptions for segments are made. Benefits of proposed approach include simplicity, computational efficiency, and adaptability to a wide range of different shapes of time series. Performance is demonstrated on synthetic and real-world data, including a large-scale dataset comprising a multivariate time series of dimension 1000 and length 2709. Proposed method is compared to a popular model-based bottom-up approach fitting piecewise affine models and to a recent model-based top-down approach fitting Gaussian models and found to be consistently faster while producing more intuitive results in the sense of segmenting time series at peaks and valleys.

Keywords: time series segmentation, model-free, trading-inspired, multivariate data

Procedia PDF Downloads 53
236 Influence of the Financial Crisis on the Month and the Trading Month Effects: Evidence from the Athens Stock Exchange

Authors: Aristeidis Samitas, Evangelos Vasileiou

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The aim of this study is to examine the month and the trading month effect under changing financial trends. We choose the Greek stock market to implement our assumption because there are clear and long term periods of financial growth and recession. Daily financial data from Athens Exchange General Index for the period 2002-2012 are considered. The paper employs several linear and non-linear models, although the TGARCH asymmetry model best fits in this sample and for this reason we mainly present the TGARCH results. Empirical results show that changing economic and financial conditions influences the calendar effects. Especially, the trading month effect totally changes in each fortnight according to the financial trend. On the other hand, in Greece the January effect exists during the growth periods, although it does not exist when the financial trend changes. The findings are helpful to anybody who invest and deals with the Greek stock market. Moreover, they may pave the way for an alternative calendar anomalies research approach, so it may be useful to investors who take into account these anomalies when they draw their investment strategy.

Keywords: month effect, trading month effect, economic cycles, crisis

Procedia PDF Downloads 354
235 Reexamining Contrarian Trades as a Proxy of Informed Trades: Evidence from China's Stock Market

Authors: Dongqi Sun, Juan Tao, Yingying Wu

Abstract:

This paper reexamines the appropriateness of contrarian trades as a proxy of informed trades, using high frequency Chinese stock data. Employing this measure for 5 minute intervals, a U-shaped intraday pattern of probability of informed trades (PIN) is found for the CSI300 stocks, which is consistent with previous findings for other markets. However, while dividing the trades into different sizes, a reversed U-shaped PIN from large-sized trades, opposed to the U-shaped pattern for small- and medium-sized trades, is observed. Drawing from the mixed evidence with different trade sizes, the price impact of trades is further investigated. By examining the relationship between trade imbalances and unexpected returns, larges-sized trades are found to have significant price impact. This implies that in those intervals with large trades, it is non-contrarian trades that are more likely to be informed trades. Taking account of the price impact of large-sized trades, non-contrarian trades are used to proxy for informed trading in those intervals with large trades, and contrarian trades are still used to measure informed trading in other intervals. A stronger U-shaped PIN is demonstrated from this modification. Auto-correlation and information advantage tests for robustness also support the modified informed trading measure.

Keywords: contrarian trades, informed trading, price impact, trade imbalance

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234 The Phenomenon of Nutrition as a 'Trading Zone' Approach in the Paradigm Shift between Humoral Theory and Modern Medicine

Authors: Dilay Merve Temur

Abstract:

How knowledge is produced and how scientific knowledge progress are questions that science philosophers have investigated for centuries. When the scientific and technological developments reached the 20th century, Kuhn proposed a completely new view among all the approaches. In this article, firstly, Kuhn's theory is represented. Secondly, the criticisms of Kuhn's theory directed to him are examined, and Galison's proposal for the trade area term of the incommensurability thesis is shared. The interaction of Humoral Theory with nutrition has been illustrated extensively, and the transition to modern medicine has been described historically by including scientific and technological developments in the field of medicine. This paper will seek to see how the concept of nutrition is positioned as a trading zone within the medicine paradigm, which has experienced a revolution within the framework of the paradigm concept introduced by Kuhn.

Keywords: food studies, incommensurability, nutrition and dietetics, trading zone

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