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Sentiment Analysis For Informed Profitable Trading In Boston
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By Dev Shah Dev Shah Scilit Preprints.org Google Scholar , Haruna Isah Haruna Isah Scilit Preprints.org Google Scholar * and Farhana Zulkernine Farhana Zulkernine Scilit Preprints.org Google Scholar
Received: March 4, 2019 / Revised: April 29, 2019 / Accepted: May 15, 2019 / Published: May 27, 2019
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Stock market predictions have always attracted the attention of many analysts and researchers. Popular theories suggest that stock markets are basically a random walk and it’s a fool’s game to try to predict them. Forecasting stock prices is a challenging problem in itself due to the number of variables that are involved. In the short term, the market acts like a voting machine, but in the long term, it acts as a weight, and therefore there is scope for predicting market movements over a longer period of time. The application of machine learning techniques and other algorithms to analyze and forecast stock prices is an area that holds great promise. In this article, we first provide a brief overview of stock markets and a taxonomy of stock market prediction methods. We will then focus on some of the research results in stock analysis and prediction. We discuss technical, fundamental, short-term and long-term approaches used to analyze stocks. Finally, we present some challenges and opportunities for research in this area.
Financial markets are one of the most fascinating inventions of our time. They have had a significant impact on many areas such as business, education, jobs, technology and thus the economy (Hiransha et al. 2018). Over the years, investors and researchers have been interested in developing and testing models of stock price behavior (Fama 1995). However, the analysis of stock market movements and price behavior is extremely challenging because markets are dynamic, non-linear, non-stationary, non-parametric, noisy and chaotic (Abu-Mostafa and Atiya 1996). According to Zhong and Enke (2017), stock markets are influenced by many highly interrelated factors that include economic, political, psychological, and society-specific variables. Technical and fundamental analysis are two main approaches to financial market analysis (Park and Irwin 2007; Nguyen et al. 2015). Investors have used these two main decision-making approaches in financial markets to invest in stocks and achieve high profits with low risk (Arévalo et al. 2017).
According to Hu et al. (2015), fundamental analysis is mainly based on three basic aspects (i) macroeconomic analysis, such as gross domestic product (GDP) and consumer price index (CPI), which analyzes the impact of the macroeconomic environment on the company’s future profit, (ii) industry analysis , which estimates a company’s value based on the state and outlook of the industry, and (iii) company analysis, which analyzes a company’s current operations and financial condition to assess its intrinsic value. There are different valuation approaches for fundamental analysis. The average growth approximation technique compares A stock to other stocks in the same category to better understand valuations, i.e. j. assuming two companies have the same growth rate, the one with the lower price-to-earnings (P/E) ratio is considered. to be better. So the fair price is the target P/E of earnings times. The P/E method is the most commonly used valuation method in the securities brokerage industry (Imam et al. 2008). A constant growth approximation technique such as the Gordon growth model (Gordon and Shapiro 1956; Gordon 1959) is one of the best known classes of dividend discounting models. It assumes that the company’s dividends will increase forever at a constant growth rate, but less than the discount rate. Dutta et al. (2012) demonstrated the utility of fundamental analysis through the use of financial ratios to separate good stocks from poor ones. The authors compared their one-year return with the benchmark – i.e. j. Nifty – which gives an accuracy of 74.6%. This is one of the few papers that focuses on using basis functions (i.e., company-specific ratios) to identify stocks for investment.
In addition, Hu et al. (2015) grouped the areas of technical analysis into sentiment, funds flow, raw data, trend, momentum, volume, cycle and volatility. Sentiment represents the behavior of various market participants. Flow-of-funds is a type of indicator that is used to examine the financial status of various investors to pre-evaluate their strength in terms of buying and selling shares, then corresponding strategies such as short squeeze can be adopted. Raw data includes stock price series and price patterns such as K-line charts and bar charts. Trend and Momentum are examples of price-based indicators, trend is used to track stock price trends, while momentum is used to evaluate the rate of price change and assess whether a trend reversal is occurring in a stock price. Volume is an indicator that reflects the enthusiasm of both buyers and sellers for investing, it is also the basis for predicting the movement of stock prices. The cycle is based on the theory that share prices change periodically in the form of a long cycle of more than 10 years containing short cycles of a few days or weeks. Finally, volatility is often used to examine the fluctuating range of stock prices and to assess risk and identify support and resistance levels.
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Sentiments can lead to short-term swings in the market, which in turn causes a mismatch between the price and the true value of a company’s stock, but over a long period of time, the scales are triggered because the company’s fundamentals ultimately drive the value and market price of its stock. actions converge. A notable example is Nobel laureate Robert Shiller, who showed that stock prices are extremely volatile in the short term, but somewhat predictable based on price-to-earnings ratios over long periods (Shiller 1980). Diamond (2000) explained what returns can be expected from stock markets given the economic scenario and suggested that future returns could be substantially lower. Shiller (2000) also suggested that stocks are overvalued and the bubble will burst at any time. In 2000, we rightfully witnessed the bursting of the dotcom bubble.
Predicting stock market prices is a tricky business. Over the years, several theories regarding stock markets have been developed. They either try to explain the nature of the stock markets or they try to explain whether the markets can be beaten. One such popular and most debated theory, given by Fama (1970), is the Efficient Market Hypothesis (EMH), which states that at any point in time, the market price of a stock includes all information about that stock. In other words, stocks are priced exactly until something changes. There are three variants of the EMH (i) the weak form, which is consistent with the random walk hypothesis (Fama 1995), and that stock prices move randomly, while price changes are independent of each other, and therefore it is not possible to beat the market by obtaining abnormal returns on based on technical analysis; (ii) the semi-strong form, which states that prices adjust rapidly to market and public information such as dividends, earnings announcements, and political or economic events, and therefore it is not possible to obtain abnormal returns based on fundamental analysis; and finally (iii) the strong form, which says that prices reflect market, public and private information as such, no investor has monopoly access to information (Naseer and Tariq 2015).
According to the EMH, price changes are unpredictable and predicting the financial market is hopeless. However, (Abu-Mostafa and Atiya 1996) argued that the existence of so many price trends in financial markets and undiscounted serial correlations between fundamental events and economic numbers affecting markets are two of the many pieces of evidence against the EMH. Researchers and investors disagree with the EMH empirically or theoretically, shifting the focus of the debate from the EMH to the behavioral and psychological aspects of market players (Naseer and Tariq 2015). According to Zhong and Enke (2017), financial variables such as stock prices, stock market index values and
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