Search results for: stock forecasting

Authors: Lethamaga Tladi, Ray Kekwaletswe

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This study sought to conceptualise a multi-level framework for the effective use of stock ordering system in small enterprises in a rural area context. The interpretive research methodology has been used to enable the researcher to analyse, in-depth, and the subjective meanings of small enterprises’ employees in using the stock ordering system. The empirical data was collected from 13 small enterprises’ employees as participants through semi-structured interviews and observations. Interpretive Phenomenological Analysis (IPA) approach was used to analyse the small enterprises’ employee’s own account of lived experiences in relations to stock ordering system use in terms of their relatedness to, and cognitive engagement with. A case study of Kgautswane, a rural area in Limpopo Province, South Africa, served as a social context where the phenomenon manifested. Technology-Organisation-Environment Theory (TOE), Technology-to-Performance Chain Model (TPC), and Representation Theory (RT) underpinned this study. In this multi-level study, the findings revealed that; At the organisational level, the effective use of stock ordering system was found to be associated with the organisational performance gains such as efficiency, productivity, quality, competitiveness, and market share. Equally so, at the individual level, the effective use of stock ordering system minimised the end-user’s efforts and time to accomplish their tasks, which yields improved individual performance. The Multi-level framework for effective use of stock ordering system was presented.

Keywords: effective use, multi-dimensions of use, multi-level of use, multi-level research, small enterprises, stock ordering system

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Authors: Yosra Mefteh Rekik

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A computational agent-based model of financial markets stresses interactions and dynamics among a very diverse set of traders. The growing body of research in this area relies heavily on computational tools which by-pass the restrictions of an analytical method. The main goal of this research is to understand how the stock market operates and behaves how to invest in the stock market and to study traders’ behavior within the context of the artificial stock markets populated by heterogeneous agents. All agents are characterized by adaptive learning behavior represented by the Artificial Neuron Networks. By using agent-based simulations on artificial market, we show that the existence of heterogeneous agents can explain the price dynamics in the financial market. We investigate the relation between market diversity and market efficiency. Our empirical findings demonstrate that greater market heterogeneity play key roles in market efficiency.

Keywords: agent-based modeling, artificial stock market, heterogeneous expectations, financial stylized facts, computational finance

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Authors: Chih-Hsiang Chang, Fang-Jyun Su

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This paper explores whether stock characteristics influence the herding formation among investors in the US equity market. To extend the research scope of the existing literature, this paper further examines the role that stock risk characteristics play in the US equity market, and the way they influence investors’ decision-making. First, empirical results show that whether general stocks or high-risk stocks, there are no herding behaviors among the investors in the US equity market during the whole research period or during four great events. Moreover, stock characteristics have great influence on investors’ trading decisions. Finally, there is a bidirectional lead-lag relationship of the herding formation between high-risk stocks and low-risk stocks, but the influence of high-risk stocks on the low-risk stocks is stronger than that of low-risk stocks on the high-risk stocks.

Keywords: stock characteristics, herding formation, investment decision, US equity market, lead-lag relationship

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Miriam Sosa, Edgar Ortiz, Alejandra Cabello

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This paper examines the volatility changes and leverage effects of the MILA stock markets and their changes since the 2007 global financial crisis. This group integrates the stock markets from Chile, Colombia, Mexico and Peru. Volatility changes and leverage effects are tested with a symmetric GARCH (1,1) and asymmetric TARCH (1,1) models with a dummy variable in the variance equation. Daily closing prices of the stock indexes of Chile (IPSA), Colombia (COLCAP), Mexico (IPC) and Peru (IGBVL) are examined for the period 2003:01 to 2015:02. The evidence confirms the presence of an overall increase in asymmetric market volatility in the Peruvian share market since the 2007 crisis.

Keywords: financial crisis, Latin American Integrated Market, TARCH, GARCH

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Authors: Daniel Fulus Fom, Gau Patrick Damulak

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In this study, the Autoregressive Fractional Integrated Moving Average (ARFIMA) and Jordan Recurrent Neural Network (JRNN) models were employed to model the forecasting performance of the daily turbidity flow of White Clay Creek (WCC). The two methods were applied to the log difference series of the daily turbidity flow series of WCC. The measurements of error employed to investigate the forecasting performance of the ARFIMA and JRNN models are the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The outcome of the investigation revealed that the forecasting performance of the JRNN technique is better than the forecasting performance of the ARFIMA technique in the mean square error sense. The results of the ARFIMA and JRNN models were obtained by the simulation of the models using MATLAB version 8.03. The significance of using the log difference series rather than the difference series is that the log difference series stabilizes the turbidity flow series than the difference series on the ARFIMA and JRNN.

Keywords: auto regressive, mean absolute error, neural network, root square mean error

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Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Qiang Bu

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This paper compares the explanatory power of sentiment level and sentiment shock. The preliminary test results show that sentiment shock plays a more significant role in explaining stocks returns, including the raw return and abnormal return. We also find that sentiment shock beta has a higher statistical significance than sentiment beta. These finding sheds new light on the relationship between investor sentiment and stock returns.

Keywords: sentiment level, sentiment shock, explanatory power, abnormal stock return, beta

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Authors: Syed Jawad Hussain Shahzad, Muhammad Zakaria, Mobeen Ur Rehman, Saniya Khaild

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This study examines the co-movement between the Pakistan, Indian, S&P 500 and Nikkei 225 stock markets using weekly data from 1998 to 2013. The time-frequency relationship between the selected stock markets is conducted by using measures of continuous wavelet power spectrum, cross-wavelet transform and cross (squared) wavelet coherency. The empirical evidence suggests strong dependence between Pakistan and Indian stock markets. The co-movement of Pakistani index with U.S and Japanese, the developed markets, varies over time and frequency where the long-run relationship is dominant. The results of cross wavelet and wavelet coherence analysis indicate moderate covariance and correlation between stock indexes and the markets are in phase (i.e. cyclical in nature) over varying durations. Pakistan stock market was lagging during the entire period in relation to Indian stock market, corresponding to the 8~32 and then 64~256 weeks scale. Similar findings are evident for S&P 500 and Nikkei 225 indexes, however, the relationship occurs during the later period of study. All three wavelet indicators suggest strong evidence of higher co-movement during 2008-09 global financial crises. The empirical analysis reveals a strong evidence that the portfolio diversification benefits vary across frequencies and time. This analysis is unique and have several practical implications for regional and international investors while assigning the optimal weightage of different assets in portfolio formulation.

Keywords: co-movement, Pakistan stock exchange, S&P 500, Nikkei 225, wavelet analysis

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Authors: Zain Ul Abideen

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The study examines the potentially dark and bright parts of behavioral biases in investors’ investment decisions in the Pakistani equity market. These biases, directly and indirectly, play a comprehensive role in controlling and deciding the investor’s investment decisions. Stock market anomalies are used as a mediator, while financial literacy is used as a moderator to check the mentioned relationship. The sample consisted of investors who have trading experience of more than two years in the stock market. The result indicates that calendar anomalies do not mediate between overconfidence bias and investment decisions. However, the study investigates the mediating role of fundamental and technical anomalies between overconfidence bias and investment decisions. Furthermore, calendar anomalies play a significant role between the disposition effect and investment decisions. Calendar anomalies also mediate between herding bias and investment decisions. Financial literacy significantly moderates between behavioral biases and stock market anomalies. This research would be beneficial for individual and professional investors in their investment decisions. They should be financially literate, consequently less biased and have no market anomalies. Investors in emerging and developed economies can make optimal decisions in their respective stock markets.

Keywords: behavioral biases, financial literacy, stock market anomalies, investment decision

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Authors: Rosyidah, Permata Wulandari

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We investigate the price effect of addition and deletions from the Indonesia Sharia Stock Index (ISSI) and Jakarta Islamic Index (JII). Using event study methodology, we measure abnormal returns for firms over the period June 2019 - to December 2021. Through the sample of 107 additions and 95 deletions, we find evidence to support the theory of Muslim country investment behavior. We find that additions to the Islamic index led to a significant positive stock market reaction and deletions to the Islamic index led to a negative stock market reaction on Jakarta Islamic Index (JII) and there is no significant reaction of addition and deletion on Indonesia Sharia Stock Index (ISSI).

Keywords: abnormal return, abnormal volume, event study, index changes, sharia index

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Authors: Yousef Abu Nahleh, Arun Kumar, Fugen Daver, Reham Al-Hindawi

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Despite the availability of natural disaster related time series data for last 110 years, there is no forecasting tool available to humanitarian relief organizations to determine forecasts for emergency logistics planning. This study develops a forecasting tool based on identifying probability of disaster for each country in the world by using decision tree modeling. Further, the determination of aggregate forecasts leads to efficient pre-disaster planning. Based on the research findings, the relief agencies can optimize the various resources allocation in emergency logistics planning.

Keywords: decision tree modeling, forecasting, humanitarian relief, emergency supply chain

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Authors: Mohammad Azam

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The purpose of this study is to investigate risk and real excess portfolio returns using inflation adjusted risk-free rates, a measuring technique that focuses on the momentum augmented Fama-French six-factor model and use monthly data from 1994 to 2022. With the exception of profitability, the data show that market, size, value, momentum, and investment factors are all strongly associated to excess portfolio stock returns using ordinary lease square regression technique. According to the Gibbons, Ross, and Shanken test, the momentum augmented Fama-French six-factor model outperforms the market. This technique discovery may be utilised by academics and professionals to acquire an in-depth knowledge of the Pakistan Stock Exchange across a broad stock pattern for investing decisions and portfolio construction.

Keywords: real excess portfolio returns, momentum augmented fama & french five-factor model, GRS-test, pakistan stock exchange

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Authors: Ogunwole Cecilia Oluwakemi, Wahid Damilola Olanipekun, Omoyele Olufemi Samuel, Timothy Ayomitunde Aderemi

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The issues relating to corporate governance in both locally and internationally managed firms cannot be overemphasized because the lack of efficient corporate governance could orchestrate serious problems in any organization. Against this backdrop, this study examines the nexus between corporate governance and performance of firms from 2012 to 2020, using the case study of the Nigerian stock exchange. Consequently, data was collected from forty (40) listed firms on the Nigerian Stock Exchange. The study employed a fixed effect technique of estimation to address the objective of the study. It was discovered from the study that the influence of corporate governance components such as gender diversity, board independence and managerial ownership led to a significant positive impact on the performance of the firms under the investigation. In view of the above finding, this study makes the following recommendations for the policymakers in Nigeria that anytime the goal of the policymakers is the improvement of performance of the listed firms in the Nigerian stock exchange, board independence and a balance in the inclusion of male and female among the board of directors should be encouraged in these firms.

Keywords: corporate, governance, firms, performance, Nigeria, stock, exchange

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Authors: Saleem Abuleil, Khalid S. Alsamara

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Events represent a significant source of information on the web; they deliver information about events that occurred around the world in all kind of subjects and areas. These events can be collected and organized to provide valuable and useful information for decision makers, researchers, as well as any person seeking knowledge. In this paper, we discuss an ongoing research to target stock markets domain to observe and record changes (events) when they happen, collect them, understand the meaning of each one of them, and organize the information along with meaning in a well-structured format. By using Semantic Role Labeling (SRL) technique, we identified four factors for each event in this paper: verb of action and three roles associated with it, entity name, attribute, and attribute value. We have generated a set of rules and techniques to support our approach to analyze and understand the meaning of the events taking place in stock markets.

Keywords: natuaral language processing, Arabic language, event extraction and understanding, sematic role labeling, stock market

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Authors: H. J. Joshi, Satyajeet Patil, Parth Dandavate, Mihir Kulkarni, Harshita Agrawal

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As the world looks towards a sustainable future, electric vehicles have become increasingly popular. Millions worldwide are looking to switch to Electric cars over the previously favored combustion engine-powered cars. This demand has seen an increase in Electric Vehicle Charging Stations. The big challenge is that the randomness of electrical energy makes it tough for these charging stations to provide an adequate amount of energy over a specific amount of time. Thus, it has become increasingly crucial to model these patterns and forecast the energy needs of power stations. This paper aims to analyze how different machine learning models perform on Electric Vehicle charging time-series data. The data set consists of authentic Electric Vehicle Data from the Netherlands. It has an overview of ten thousand transactions from public stations operated by EVnetNL.

Keywords: forecasting, smart grid, electric vehicle load forecasting, machine learning, time series forecasting

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Authors: Jayanta Pokharel, Gokarna Aryal, Netra Kanaal, Chris Tsokos

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Investment in stocks and shares aims to seek potential gains while weighing the risk of future needs, such as retirement, children's education etc. Analysis of the behavior of the stock market returns and making prediction is important for investors to mitigate risk on investment. Historically, the normal variance models have been used to describe the behavior of stock market returns. However, the returns of the financial assets are actually skewed with higher kurtosis, heavier tails, and a higher center than the normal distribution. The Laplace distribution and its family are natural candidates for modeling stock returns. The Variance-Gamma (VG) distribution is the most sought-after distributions for modeling asset returns and has been extensively discussed in financial literatures. In this paper, it explore the other Laplace family, such as Asymmetric Laplace, Skewed Laplace, Kumaraswamy Laplace (KS) together with Variance-Gamma to model the weekly returns of the S&P 500 Index and it's eleven business sector indices. The method of maximum likelihood is employed to estimate the parameters of the distributions and our empirical inquiry shows that the Kumaraswamy Laplace distribution performs much better for stock returns modeling among the choice of distributions used in this study and in practice, KS can be used as a strong alternative to VG distribution.

Keywords: stock returns, variance-gamma, kumaraswamy laplace, maximum likelihood

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Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

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Authors: Lindrianasari Stefanie, Aminah Khairudin

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This study aims to provide empirical evidence regarding the ability of Price to Earnings Ratio and PEG Ratio in predicting future stock returns issuers. The samples used in this study are stocks that go into LQ45. The main contribution is to assign empirical evidence if the PEG Ratio can provide optimum return compared to Price to Earnings Ratio. This study used a sample of the entire company into the group LQ45 with the period of observation. The data used is limited to the financial statements of a company incorporated in LQ45 period July 2013-July 2014, using the financial statements and the position of the company's closing stock price at the end of 2010 as a reference benchmark for the growth of the company's stock price compared to the closing price of 2013. This study found that the method of PEG Ratio can outperform the method of PE ratio in predicting future returns on the stock portfolio of LQ45.

Keywords: price to earnings growth, price to earnings ratio, future returns, stock price

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Authors: Aswathi Thrivikraman, S. Advaith

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The study is aimed at designing encoders for deciphering intricacies in time series data by redescribing the dynamics operating on a lower-dimensional manifold. A semi-supervised LSTM autoencoder is devised and investigated to see if the latent representation of the time series data can better forecast the data. End-to-end training of the LSTM autoencoder, together with another LSTM network that is connected to the latent space, forces the hidden states of the encoder to represent the most meaningful latent variables relevant for forecasting. Furthermore, the study compares the predictions with those of a traditional seq2seq model.

Keywords: LSTM, autoencoder, forecasting, seq2seq model

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Authors: Nikolaos Konstantopoulos, Aristeidis Samitas, Vasileiou Evangelos

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The purpose of this study is to examine if the financial trends influence not only the stock markets’ returns, but also their anomalies. We choose to study the day of the week effect (DOW) for the Greek stock market during the Euro period (2002-12), because during the specific period there are not significant structural changes and there are long term financial trends. Moreover, in order to avoid possible methodological counterarguments that usually arise in the literature, we apply several linear (OLS) and nonlinear (GARCH family) models to our sample until we reach to the conclusion that the TGARCH model fits better to our sample than any other. Our results suggest that in the Greek stock market there is a long term predisposition for positive/negative returns depending on the weekday. However, the statistical significance is influenced from the financial trend. This influence may be the reason why there are conflict findings in the literature through the time. Finally, we combine the DOW’s empirical findings from 1985-2012 and we may assume that in the Greek case there is a tendency for long lived turn of the week effect.

Keywords: day of the week effect, GARCH family models, Athens stock exchange, economic growth, crisis

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Authors: Benny Benyamin Nasution, Rahmat Widia Sembiring, Abdul Rahman Dalimunthe, Nursiah Mustari, Nisfan Bahri, Berta br Ginting, Riadil Akhir Lubis, Rita Tavip Megawati, Indri Dithisari

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The earth coordinate system is an important part of an attempt for earthquake forecasting, such as the one using Single Layer Hierarchical Graph Neuron (SLHGN). However, there are a number of problems that need to be worked out before the coordinate system can be utilized for the forecaster. One example of those is that SLHGN requires that the focused area of an earthquake must be constructed in a grid-like form. In fact, within the current earth coordinate system, the same longitude-difference would produce different distances. This can be observed at the distance on the Equator compared to distance at both poles. To deal with such a problem, a coordinate system has been developed, so that it can be used to support the ongoing earthquake forecasting using SLHGN. Two important issues have been developed in this system: 1) each location is not represented through two-value (longitude and latitude), but only a single value, 2) the conversion of the earth coordinate system to the x-y cartesian system requires no angular formulas, which is therefore fast. The accuracy and the performance have not been measured yet, since earthquake data is difficult to obtain. However, the characteristics of the SLHGN results show a very promising answer.

Keywords: hierarchical graph neuron, multidimensional hierarchical graph neuron, single layer hierarchical graph neuron, natural disaster forecasting, earthquake forecasting, earth coordinate system

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Authors: Riyadh Jamegh, Alla Eldin Kassam, Sawsan Sabih

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In order to overcome uncertainty conditions and inability to meet customers' requests due to these conditions, organizations tend to reserve a certain safety stock level (SSL). This level must be chosen carefully in order to avoid the increase in holding cost due to excess in SSL or shortage cost due to too low SSL. This paper used soft computing fuzzy logic to identify optimal SSL; this fuzzy model uses the dynamic concept to cope with high complexity environment status. The proposed model can deal with three input variables, i.e., demand stability level, raw material availability level, and on hand inventory level by using dynamic fuzzy logic to obtain the best SSL as an output. In this model, demand stability, raw material, and on hand inventory levels are described linguistically and then treated by inference rules of the fuzzy model to extract the best level of safety stock. The aim of this research is to provide dynamic approach which is used to identify safety stock level, and it can be implanted in different industries. Numerical case study in the dairy industry with Yogurt 200 gm cup product is explained to approve the validity of the proposed model. The obtained results are compared with the current level of safety stock which is calculated by using the traditional approach. The importance of the proposed model has been demonstrated by the significant reduction in safety stock level.

Keywords: inventory optimization, soft computing, safety stock optimization, dairy industries inventory optimization

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Authors: Flavio Cordeiro Fontanella, Asla Medeiros e Sá, Moacyr Alvim Horta Barbosa da Silva

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In the present work, we analyse the performance of football results forecasting models. In order to do so, we have performed the data collection from eight different forecasting models during the 2021 Brazilian football season. First, we guide the analysis through visual representations of the data, designed to highlight the most prominent features and enhance the interpretation of differences and similarities between the models. We propose using a 2-simplex triangle to investigate visual patterns from the results forecasting models. Next, we compute the expected points for every team playing in the championship and compare them to the final league standings, revealing interesting contrasts between actual to expected performances. Then, we evaluate forecasts’ accuracy using the Ranked Probability Score (RPS); models comparison accounts for tiny scale differences that may become consistent in time. Finally, we observe that the Wisdom of Crowds principle can be appropriately applied in the context, driving into a discussion of results forecasts usage in practice. This paper’s primary goal is to encourage football forecasts’ performance discussion. We hope to accomplish it by presenting appropriate criteria and easy-to-understand visual representations that can point out the relevant factors of the subject.

Keywords: accuracy evaluation, Brazilian championship, football results forecasts, forecasting models, visual analysis

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Authors: N. Sopipan

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In this paper, we study of rainfall time series of weather stations in Nakhon Ratchasima province in Thailand using various statistical methods enabled to analyse the behaviour of rainfall in the study areas. Time-series analysis is an important tool in modelling and forecasting rainfall. ARIMA and Holt-Winter models based on exponential smoothing were built. All the models proved to be adequate. Therefore, could give information that can help decision makers establish strategies for proper planning of agriculture, drainage system and other water resource applications in Nakhon Ratchasima province. We found the best perform for forecasting is ARIMA(1,0,1)(1,0,1)12.

Keywords: ARIMA Models, exponential smoothing, Holt-Winter model

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

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This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

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Authors: Bisi S. Olawoyin

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This study attempts an exploration into behavioural finance in which the traditional assumptions of expected utility maximization with rational investors in efficient markets are dropped. It reviews prior research and evidence about how psychological biases affect investors behaviour and stock selection. This study examined the relationship between demographic variables and financial behaviour biases among public sector retirees who invested in the Nigerian Stock Exchange prior to their retirement. By using questionnaire survey method, a total of 214 valid convenient samples were collected in order to determine how specific demographic and psychological trait affect stock selection between dividend paying and non-dividend paying stocks. Descriptive statistics and OLS were used to analyse the results. Findings showed that most of the retirees prefer dividend paying stocks in few years preceding their retirement but still hold on to their non-dividend paying stock on retirement. A significant difference also exists between senior and junior retirees in preference for non-dividend paying stocks. These findings are consistent with the clientele theories of dividend.

Keywords: behavioural finance, clientele theories, dividend paying stocks, stock selection

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Fuchun Li, Hong Xiao

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We propose a new early warning model for predicting financial stress events for a given future time. In this model, we examine whether credit conditions play an important role as a nonlinear propagator of shocks when predicting the likelihood of occurrence of financial stress events for a given future time. This propagation takes the form of a threshold regression in which a regime change occurs if credit conditions cross a critical threshold. Given the new early warning model for financial stress events, we evaluate the performance of this model and currently available alternatives, such as the model from signal extraction approach, and linear regression model. In-sample forecasting results indicate that the three types of models are useful tools for predicting financial stress events while none of them outperforms others across all criteria considered. The out-of-sample forecasting results suggest that the credit-regime switching model performs better than the two others across all criteria and all forecasting horizons considered.

Keywords: cut-off probability, early warning model, financial crisis, financial stress, regime-switching model, forecasting horizons

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