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Expectations of Returns and Expected Returns
We analyze time series of investor expectations of future stock market returns from six data sources between 1963 and 2011. The six measures of expectations are highly positively correlated with each other, as well as with past stock returns and with the level of the stock market. However, investor expectations are strongly negatively correlated with model-based expected returns. The evidence is not consistent with rational expectations representative investor models of returns.
Journal Article
Understanding the Growth of African Financial Markets
This paper examines empirically the determinants of financial market development in Africa with an emphasis on banking systems and stock markets. The results show that income level, creditor rights protection, financial repression, and political risk are the main determinants of banking sector development in Africa, and that stock market liquidity, domestic savings, banking sector development, and political risk are the main determinants of stock market development. We also find that liberalizing the capital account promotes financial market development only in countries with high incomes, well- developed institutions, or both. The powerful impacts of political risk on both banking sector and stock market development suggest that resolution of political risk may be important to the development of African financial markets.
eBook
Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications
The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models.
Journal Article
Deep Learning for Stock Market Prediction
The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.
Journal Article
DEPRESSION BABIES: DO MACROECONOMIC EXPERIENCES AFFECT RISK TAKING?
We investigate whether individual experiences of macroeconomic shocks affect financial risk taking, as often suggested for the generation that experienced the Great Depression. Using data from the Survey of Consumer Finances from 1960 to 2007, we find that individuals who have experienced low stock market returns throughout their lives so far report lower willingness to take financial risk, are less likely to participate in the stock market, invest a lower fraction of their liquid assets in stocks if they participate, and are more pessimistic about future stock returns. Those who have experienced low bond returns are less likely to own bonds. Results are estimated controlling for age, year effects, and household characteristics. More recent return experiences have stronger effects, particularly on younger people.
Journal Article
Survey of feature selection and extraction techniques for stock market prediction
In stock market forecasting, the identification of critical features that affect the performance of machine learning (ML) models is crucial to achieve accurate stock price predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We find that correlation criteria, random forest, principal component analysis, and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.
Journal Article
The Media and the Diffusion of Information in Financial Markets: Evidence from Newspaper Strikes
The media are increasingly recognized as key players in financial markets. I investigate their causal impact on trading and price formation by examining national newspaper strikes in several countries. Trading volume falls 12% on strike days. The dispersion of stock returns and their intraday volatility are reduced by 7%, while aggregate returns are unaffected. Moreover, analysis of return predictability indicates that newspapers propagate news from the previous day. These findings demonstrate that the media contribute to the efficiency of the stock market by improving the dissemination of information among investors and its incorporation into stock prices.
Journal Article
Power Laws in Economics: An Introduction
Many of the insights of economics seem to be qualitative, with many fewer reliable quantitative laws. However a series of power laws in economics do count as true and nontrivial quantitative laws—and they are not only established empirically, but also understood theoretically. I will start by providing several illustrations of empirical power laws having to do with patterns involving cities, firms, and the stock market. I summarize some of the theoretical explanations that have been proposed. I suggest that power laws help us explain many economic phenomena, including aggregate economic fluctuations. I hope to clarify why power laws are so special, and to demonstrate their utility. In conclusion, I list some power-law-related economic enigmas that demand further exploration. A formal definition may be useful.
Journal Article
Asset Bubbles and Credit Constraints
We provide a theory of rational stock price bubbles in production economies with infinitely-lived agents. Firms meet stochastic investment opportunities and face endogenous credit constraints. They are not fully committed to repaying debt. Credit constraints are derived from incentive constraints in optimal contracts which ensure default never occurs in equilibrium. Stock price bubbles can emerge through a positive feedback loop mechanism and cannot be ruled out by transversality conditions. These bubbles command a liquidity premium and raise investment by raising the debt limit. Their collapse leads to a recession and a stock market crash.
Journal Article