Stock Price Prediction using Machine Learning Algorithms

Machine learning has broad applications in the finance industry. Risk Analytics, Consumer Analytics, Fraud Detection, and Stock Market Predictions are some of the domains where machine learning methods can be implemented. Accurate prediction of stock market returns is extremely difficult due to volatility in the market. The main factor in predicting a stock market is a high level of accuracy and precision. With the introduction of artificial intelligence and high computational capacity, efficiency has increased. In the past few decades, the highly theoretical and speculative nature of the stock market has been examined by capturing and using repetitive patterns. Various machine learning algorithms like Multiple Linear Regression, Polynomial Regression, etc. are used here. The financial data contains factors like Date, Volume, Open, High, Low Close, and Adj Close prices. The models are evaluated using standard strategic indicators RMSE and R2 score. Lower values of these two indicators mean higher efficiency of the trained models. Various companies employ different types of analysis tools for forecasting and the primary aim is the accuracy to obtain the maximum profit. The successful prediction of the stock will be an invaluable asset for the stock market institutions and will provide real-life solutions to the problems of the investors. Keywords: Stock prices, Analysis, Accuracy, Prediction, Machine Learning, Regression, Finance

Effects of COVID-Induced Public Anxiety on European Stock Markets: Evidence From a Fear-Based Algorithmic Trading System

The effect of COVID-induced public anxiety on stock markets, particularly in European stock market returns, is examined in this research. The search volumes for the notion of COVID-19 gathered by Google Trends and Wikipedia were used as proxies for COVID-induced public anxiety. COVID-induced public anxiety was shown to be linked with negative returns in European stock markets when a panel data method was used to a sample of data from 14 European stock markets from January 2, 2020 to September 17, 2020. Using an automated trading system, we used this finding to suggest investment methods based on COVID-induced anxiety. The findings of back-testing indicate that these techniques have the potential to generate exceptional profits. These results have significant consequences for government officials, the media, and investors.

Multiscale Partial Correlation Clustering of Stock Market Returns

This study proposes a wavelet procedure for estimating partial correlation coefficients between stock market returns over different time scales. The estimated partial correlations are subsequently used in a cluster analysis to identify, for each time scale, groups of stocks that exhibit distinct market movement characteristics and are therefore useful for portfolio diversification. The proposed procedure is demonstrated using all the major S&P 500 sector indices as well as precious metals and energy sector futures returns during the last decade. The results suggest cluster formations that vary by time scale, which entails different stock selection strategies for investors differing in terms of their investment horizon orientation.

Stock Market Responses to Monetary and Fiscal Policies

This study analyzes the short-run and long-run effects of interaction between fiscal and monetary policies on stock market performance in four emerging Asian economies, which are China, India, Indonesia, and Malaysia, by using ARDL model. The study covers the period of 2003:Q1-2020:Q1. The findings from this study show monetary and fiscal policies play an important role in determining stock market returns. Also, the results theoretically support Richardian neutrality hypothesis for China and Indonesia, Keynesian positive effect hypothesis for India, and classical crowding out effect hypothesis for Malaysia, and interest channel of monetary transmission mechanism only for China.

Subjective Return Expectations, Perceptions, and Portfolio Choice

Exploiting a representative sample of the French population by age, wealth, and asset classes, we document novel facts about their expectations and perceptions of stock market returns. Both expectations and perceptions of returns are very dispersed, significantly lower than their data counterparts, and a substantial portion of the variation in the former is explained by dispersion in the latter. Consistent with portfolio choice models under incomplete information, a conditional risk-return trade-off explains the intensive margin, while at the extensive margin, only expected returns matter. Despite accounting for survey measurement error in subjective return expectations, ’muted sensitivities’ at both portfolio choice margins obtain, getting consistently (i) bigger when excluding informed non-participants, and (ii) smaller, for inertial and professionally delegated portfolios.

Market expectation shifts in option-implied volatilities in the US and UK stock markets during the Brexit vote

This paper investigates the effect of the Brexit vote on the connection between UK stock market expectations and US stock market returns. To gauge UK stock market expectations, the option-implied volatilities of the FTSE 100 index are calculated in the period starting five months before and ending four months after the Brexit referendum. To keep the analysis “clean”, it stops right before the 2016 US presidential elections. It uses an OLS regression to estimate the change in the relationship between US and UK stock market expectations.The main findings show that the US and UK stock markets became somewhat less integrated four months after the Brexit referendum compared to the five months before it. The S&P 500 Index returns have a statistically significant impact on implied volatilities of the FTSE 100 only before the Brexit referendum. However, the British risk-free rate (LIBOR) became a statistically significant factor affecting FTSE 100 implied volatilities only after Brexit. This analysis may be used by decision-makers in the money management industry to act appropriately during Black Swan events. When UK citizens unexpectedly voted in favor of Brexit, the risk-free rate dropped, making it cheaper to invest, increasing the Sharpe ratios of equity portfolios. Coupled with increased uncertainty, this caused portfolio reallocations. In turn, expected volatility measured by options-implied volatility increased. AcknowledgmentThe authors would like to thank Olesia Verchenko for critique, a KSE M.A., external defense reviewer for helpful comments.

THE INFLUENCES OF STOCK MARKET ON EXCHANGE-TRADED FUND UNDER THE THRESHOLD EFFECT OF MONETARY POLICY IN CHINA

This paper estimates the smooth transition autoregressive model with exogenous variables to evaluate the effects of stock market returns on the exchange-traded funds’ (ETFs) returns in China with reserve requirement ratio (RRR) from monetary policy as a transition variable. The sample used in this study lasts from March 4, 2005 to June 30, 2017. The empirical result points out that there is the effect of RRR value on the relationship between stock market returns and ETF return. Moreover, these effects are variable depending on the conversion and its changes over time in different variations of threshold intervals. Lastly, the larger the change of China’s stock market variables’ lag period, the smaller the impacts on Chinese ETF return.

The Influence of Oil Prices on Stock Market Returns in Saudi Arabian Companies: The Implementation of Econometric Models

The countries in the sample are of special importance, as they have different rates of growth, different important characteristics of the financial system and levels of stock market progress. The research looks on equity market growth and measures its foreign economic effect, not in terms of profitability to investors (not beyond the scope of our study), but in terms of progress relative to the scale of these economies and the capital expenditure fund needs of those countries. The data used in this study were taken from GCC's monthly time series over the 2008-2018 period. Such factors are actual interest rates, global development level, commodity market returns on commodities and the true price of oil (in US dollars). Thomson Reuters DataStream, Bloomberg and OECD database gather data for this study. For this study, the actual interest rate was selected as this element illustrates market swings. The Industrial Production Index has defined it since the overall energy consumption in an economy is calculated by the amount of products and services generated in the region. The research implemented and econometric approach throughout addressing data from 2008 till 2018 which means 10 years to study the impact of oil prices, exchange rates and their impact on stock market, case Saudi Arabia. The key results showed that the contemporary and postponed impacts on economic development in either capital market liquidity, as measured by turnover or economic change, as measured by the institutional efficiency index. The relationship predictor (investment / Turnover ratio) was seen for the Arab countries to have an important result from the robustness measure. Implementing the strategy of gross capital expenditure expansion and the turnover partnership will lead to a positive impact on the connection between country expenditure and stock market liquidity during the competitive growth model.