Stock Market Indices Research Articles

Technical Indicator based Stock Market Prediction using Recurrent Neural Network

The Indian stock market is highly volatile and complex by nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy & Sell prices are predictable and investor can make above-average profits using Stock Technical Indicator (STIs).Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company’s reputation, news, sentiments and other fundamental issues. In this work, architecture of project is given. As a part of prediction model the Recurrent Neural Network (RNN) Deep Learning Algorithm is used to predict future prices of Stocks Using Technical Indicators (STIs) and also buy sell signals are generated. The project will be carried on National Stock Exchange (NSE) Stocks of India.

Decoding Nifty50’s Price Enigma: Machine Learning Powered Approach

Abstract: This study uses machine learning and Long Short-Term Memory (LSTM) networks to decipher the price riddle of Nifty50, a well-known stock market index in India. Using Long Short-Term Memory (LSTM) recurrent neural networks, which are renowned for their capacity to recognise sequential patterns, we aim to forecast and comprehend the intricate dynamics influencing Nifty50's price fluctuations.

Whose firm? Resilience of the German corporate sector to financialization

Abstract We examine how financialization has progressed in the German nonfinancial corporate sector since the 2000s. Using a sample of firms historically listed in the largest German stock market indices, DAX and MDAX, we not only confirm the rise of international passive asset managers but also find a growing prevalence of controlling business families. Although executive pay increasingly consists of equity grants, indicating growing shareholder value orientation, we do not identify corporate financialization in terms of rising share buybacks and payout rates. Instead, ever larger shares of corporate funds are kept inside firms as retained earnings. While firms in the USA ‘downsize-and-distribute’ under the pressure of institutional investors, we hold that German firms ‘save-and-sit-on-it’. Although shaped by the liberalization of corporate governance regulations, this regime still relies on blockholdings and codetermination, while integrating asset managers as new providers of patient capital.

Advance Sequential Models for Asset Trading: Leveraging LSTM & RNN with Stochastic RSI and MACD Indicators

Abstract: The goal of stock market prediction is to forecast a company's financial stocks' future worth. The application of machine learning, which creates predictions based on the values of the current stock market indices by training on their prior values, is a recent trend in stock market prediction technologies. Machine learning utilises many models to facilitate accurate and reliable prediction. The study focuses on stock value prediction using regression and LSTM-based machine learning. Considered factors are volume, low, high, open, and closed. We also employ algorithmic trading, which is a technique for automatically executing orders based on pre-programmed trading instructions that take volume, price, and time into account. This kind of trading aims to take advantage of a computer's speed and computational power in comparison to human traders. Algorithmic trading has become more popular in the twenty-first century, prompting traders to respond. According to a 2019 research, 92% of traders use algorithms rather than people. The moving average convergence & divergence and the relative strength index are our primary tools. One technical tool employed in financial market analysis is the relative strength index, or RSI. Charting a stock's historical and present strength or weakness with retail and institutional traders is the goal. Investment banks, mutual funds, pension funds, and hedge funds all utilise it extensively when they need to perform trades. based on the closing prices of a recent trading period, too fast for a human or the market. It is important to distinguish the indicator from relative strength. The RSI measures the speed and size of price changes and is categorised as a momentum oscillator. The rate at which prices rise or fall is known as momentum. The ratio of higher closes to lower closes, where closures are defined as averages of absolute values of price changes, is the relative strength, or RS. The RSI determines momentum by dividing higher closes by overall closes, or stocks with more or stronger positive moves. The RSI is most commonly employed during a 14-day period and is scaled from 0 to 100, with 70 and 30 representing the high and low readings, respectively. Alternately shorter or longer outlooks are associated with shorter or longer time frames. Although they happen less frequently, high and low levels— 80 and 20, or 90 and 10—indicate increased momentum. J. Welles Wilder created the relative strength index, which was first published in the June 1978 issue of commodities magazine and in his 1978 book New concepts in technical trading systems, and in commodities magazine in the June 1978 issue.

Economic Policy Uncertainty and Stock Market Index: Fresh Insights from Augmented-ARDL and Multiple Structural Breaks

This study provides unique insights into the link between economic policy uncertainty (EPU) and stock market index in Sub-Saharan African (SSA) countries. Basically, the dearth of evidence in this perspective, especially in the context of SSA motivates this new assessment. Thus, on account of the monthly longitudinal series for 11 SSA countries over 2014M1–2023M6, the novel multiple structural break test in panel (MSBP) and cross-sectional ARDL (CS-ARDL) provide the following upshots: first, the high level of EPU worsens the region’s stock index in the short run; second, there is a long-term negative but insignificant effect of EPU on the stock index; third, EPU significantly reduces the stock index in the three identified structural break regimes, but outside it, it is insignificant; fourth, the devastating influence of EPU on stock prices is more pronounced during the COVID-19 era demonstrating that the response of the region’s stock market to EPU is highly sensitive to global shocks. The policy implication from the major findings is for the government authorities to put up plans that can strengthen the stock markets’ resilience to economic policy-induced uncertainty.

A Study on Employees Perception From Govche India Private Limited Towards Investing in Indian Stock Market

Stock market investments have become a typical occurrence for all people today. The development of the country's stock markets has a significant impact on the growth of the national economy. Global financial market developments have opened up a wide range of markets and investment opportunities for investors. Making investment decisions is a difficult undertaking that requires extensive analysis. Research has been conducted to identify the variables affecting investors' preferences and investment choices. Through the use of a structured questionnaire, primary data is acquired. The study included 107 respondents as its sample size. Data was analysed in this study using percentages, chi square, and Anova. It has been discovered that economic factors such as currency strength and legal considerations have an impact on investment choices.

Monetary Policy Spillovers and Inter-Market Dynamics Perspective of Preferred Habitat Model

This study advances the understanding of the Preferred Habitat Model’s capacity to shed light on the inter-market transfer of mean returns and the diffusion of price volatility in Pakistani investment markets. It examines the extent to which returns in one market exert a systematic influence on returns across others under the potential sway of interest rate policy shifts, USD exchange rate volatility, and domestic inflation trends. Employing a methodological arsenal that includes the GARCH process, enhanced by Dynamic Conditional Correlations (DCC), as well as the Markov Switching Model, this research assesses the propagation of mean returns and volatility across markets. The analysis uncovers significant linkages between monetary policy and stock market indices, underscoring the profound impact of monetary policy on cross-market performance transmission. These insights are pivotal for regulators overseeing the nuanced interaction between monetary policy and market performance. They are crucial for local and international investors interested in developing economies, especially in Pakistan’s markets.

An Alignment of Financial Signaling and Stock Return Synchronicity

Financial signaling and stock return synchronicity may not be at crossroads. This paper optimizes the signaling effect of firms’ financial indicators on stock return synchronicity. The ultimate objective is to align firms’ financial signaling and stock return synchronicity, which implies a benefit of hedging against fluctuations in the stock market index. The data cover quarterly periods from June 1992 to March 2022 for the non-financial firms listed in the DJIA30 and NASDAQ100. This paper examines the observed return synchronicity as the dependent variable. The independent variables are classified into six groups namely, Solvency (or Liquidity) ratios, Assets Efficiency ratios, Expense Control ratios, Debt (or Leverage) ratios, Profitability ratios, and Dividend ratios. The analysis is conducted on two different groups. The first group examines the observed firms’ financials that affect observed stock return synchronicity. The second group examines optimal firms’ financials that help optimize stock return synchronicity. The final results show that (a) current stock return synchronicity is affected positively by cash ratio, and negatively by receivables and historical growth of earnings; (b) optimal stock return synchronicity can be elevated using significant financial indicators namely, Inventory/Current Assets, Net Working Capital/Total Assets, Net worth/Fixed Assets, and Sales Annual Growth; (c) agency conflicts between managers and shareholders can be mitigated by the aforementioned financial indicators, which do not include debt financing being the common source of agency conflicts; and (d) dividends are still insignificant to stock return synchronization.

Moderating role of political stability and economic policy uncertainty between country governance practice and stock market performance. A comparative analysis of Pakistan and Kurdistan Region of Iraq.

The objective of the study is to explore the relationship between country governance practices along with political stability and Economic policy uncertainty, and stock market performance of two different economies, Pakistan and Kurdistan region of Iraq. To meet our objectives, we used the 25 years past data from 1996 to 2021. Data is collected from the DataStream database. The regression analysis is used as the method of estimation for linear and moderation effect. Our results show that regulatory quality, rules of law and political stability has significant positive relationship with stock market performance of Pakistan, but all the governance indicators have significant positive relationship with stock market performance of the Kurdistan Region of Iraq. Moreover, political stability has significant moderating impact between the governance practices and the performance of the stock markets of both economies indicating that the governance practices perform well with the political stability that leads to rise in the stock market indices of selected countries. Economic policy uncertainty has significant negative moderation impact due to creating the risk in both economies that decrease the performance of the stock markets of the selected economies. Finally, our study advocated some implications for the investors to increase their confidence on the stock of high political stability and low economic policy uncertainty economies. Government can take significant measures to control the uncertainty of the policy and portfolio managers can adjust their risk on the ground of the political stability and efficient governance practices countries.

Trading commodity ETFs: Price behavior, investment insights, and performance analysis

AbstractThis study analyzes the risk‐adjusted performance of commodity exchange‐traded funds (ETFs) across diverse market conditions. Examining monthly returns from December 2004 to June 2022, our findings suggest that commodity ETFs underperformed US stocks, indicating limited diversification benefits. However, we document positive α during turbulent market periods like the COVID‐19 crisis, signifying potential resilience. Furthermore, our factor regressions reveal that shifts in the global commodity price index and disposable personal income significantly influence commodity ETFs' excess returns, pointing to broader economic and income‐related trends. Commodity ETFs exhibit lower Value‐at‐Risk and Expected Shortfall compared to stock market indices, indicating reduced downside risk exposure for investors. Given the increasing popularity of commodity ETFs, these insights hold substantial significance for market participants.

Do Major and Emerging Economies Co-Integrate with, Influence Over, or Make a Volatility Spillover Effect on the Indian Stock Market? An Empirical Analysis

Do Major and Emerging Economies Co-Integrate with, Influence Over, or Make a Volatility Spillover Effect on the Indian Stock Market? An Empirical Analysis

Examining the Random Walk Hypothesis: An investigation of the Indian stock market

Using the Efficient Market Hypothesis (EMH) as the theoretical foundation, this research study explores the Random Walk Hypothesis in the context of the Indian stock market. By using the Runs Test to analyse the daily closing prices of eight well-known Indian companies between January 1, 2018, and June 30, 2021, the study finds that patterns in stock prices in the Indian stock market show weak-form efficiency and are consistent with the Random Walk Hypothesis. The analysis indicates that stock price movements are largely unpredictable and devoid of discernible patterns, aligning with the notions posited by proponents of the Efficient Market Hypothesis. The research underscores significant implications, validating the presence of weak-form efficiency and emphasizing the need for alternative analytical approaches in investment strategies. Despite inherent limitations, this study contributes to ongoing discourse on market efficiency and financial dynamics, advocating for interdisciplinary approaches and global perspectives to refine understanding and facilitate more informed investment decisions in the Indian capital market.

A Study on the Spill Over Effects on Nifty 50 Returns: Evidence from Stock Market Performances of Major Asian Counterparts of India

This study investigates the spillover effects of major Asian stock market performances on the returns of the Nifty 50, India's benchmark index. Employing advanced econometric techniques, we analyze the dynamic relationships between the Nifty 50 and key Asian counterparts, considering both short-term and long-term horizons. Our research delves into return spillovers aiming to comprehensively assess the interconnectedness and transmission of market movements across the region. This study utilizes an extensive dataset covering Nifty 50 returns from April 1, 2013, to March 31, 2023, along with meticulously documented returns from stock market indexes linked to major strategic alliances in India. By applying advanced multivariate time series analysis techniques, encompassing Vector Autoregression (VAR), Granger causality tests, and impulse response functions, we model the intricate dynamics interweaving these indexes with the Nifty 50. The research endeavors to answer fundamental questions, including assessing short- term and long-term effects resulting from major Asian counterparts of India on Nifty returns, as well as exploring noticeable patterns that indicate the direction of causality between these counterparts and Nifty returns. The outcomes of this study carry substantial implications for decision-makers. Investors can make more informed decisions, businesses planning strategic partnerships can refine their strategies, and policymakers can formulate economic and financial regulations that accurately reflect the complexities of these dynamics. By unveiling the ripple effects of these counterparts on the Nifty 50, this research offers valuable insights into the evolving dynamics of India's financial markets and their integration into the global economy.

A 16-Year Analysis of Aesthetic Surgery Volume and Its Association with U.S. Economic Performance

Abstract Goals/Purpose Historically, demand for plastic surgery has been associated with the performance of the United States (US) economy. Over the past two decades, the US has weathered a recession, several presidential election cycles, and has seen a rise in the use and influence of social media. Each of these events has led to varying effects on the economy, which undoubtedly have had an impact on plastic surgery demand and expenditures. Thus, this study aims to evaluate the relationship between indicators of economic performance and the popularity and profitability of aesthetic surgery from 2006 to 2022. Methods/Technique Data from the Aesthetic Society’s (AS) Aesthetic Plastic Surgery National Databank and the American Society of Plastic Surgeons’ (ASPS) Plastic Surgery Statistics Report was collected from 2006 to 2022. Surgical procedures analyzed included the most commonly performed cosmetic surgeries: breast augmentation, breast reduction, mastopexy, abdominoplasty, liposuction, blepharoplasty, facelift, and rhinoplasty. The non-surgical botox injections and dermal fillers were also included. A total of 24 variables were examined, including the 10 procedures and two composite variables—total surgical procedures and total injectables; case change percent difference and patient expenditures percent difference were recorded for each of these. Within the corresponding period, economic indices were collected, including personal disposable income per capita (PDI), consumer price index (CPI), medical care services CPI, average gross domestic product per capita (GDP), and annual average closing prices of the NASDAQ, S&P 500, and the Dow Jones (DOW). Pearson correlation tests were used to analyze the strength of association between each financial indicator and case volumes and expenditures for each procedure included in the ASPS and AS reports. Results/Complications From 2006 to 2020 ASPS data demonstrated GDP year-over-year (YOY) change that was positively correlated with case volume and expenditures across 13 out of the 24 different procedure metrics (54.2%). From 2006 to 2016, AS data was positively correlated with the performance of NASDAQ, S&P500, and DOW in 12 of the 24 procedure metrics (50%). GDP YOY change closely followed suit with positive correlations to 11 variables (45.8%). YOY change of PDI, CPI, and medical care services CPI were less frequently associated amongst both data sets. For ASPS data, YOY medical services CPI change was negatively associated with five variables, indicating that as medical services CPI increased, volume of procedures and expenditures decreased. PDI YOY change was not significantly associated with any variables. In the AS dataset between 2006 and 2016, CPI YOY change was not found to be significantly associated with any variables. Across 2019–2022, CPI YOY and disposable YOY change were found to be significantly associated with three variables each. Conclusion Our study suggests that aesthetic plastic surgery procedures and expenditures are most positively correlated with GDP. Stock market indices are additional financial indicators which have significant positive correlations with plastic surgery case volume and expenditures. Inflation indicators were less frequently correlated. Over this timeframe, several important financial events have occurred including the Great Recession, a stock market selloff in 2015 and 2016, and the COVID-19 pandemic. Each of these events, along with several election cycles and the increasing influence of social media and popular culture, have had variable impacts on consumers and the demand for aesthetic plastic surgery. Plastic surgeons should be familiar with US and global financial markets as they fluctuate with current events. While plastic surgery prices and demand may be difficult to anticipate, this study elucidates several factors that may be used by plastic surgeons as a bellwether for their practice.

Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak

Economic Policy Uncertainty and Volatility Spillovers Among International Stock Market Indices During the COVID-19 Outbreak

News-driven stock market index prediction based on trellis network and sentiment attention mechanism

Applying deep learning, especially time series neural network, to stock market prediction, has become one of the important applications in the quantitative finance field. However, due to the multi-correlation and volatility of the stock market, how to timely and accurately predict it has become a challenging issue. In order to cope with this challenge, a news-driven stock market index prediction model based on TrellisNet and a sentiment attention mechanism (SA-TrellisNet) is proposed. A sentiment analysis model based on CNN and LSTM is presented to obtain the sentiment index of massive news crawled from authoritative financial websites. Furthermore, a sentiment attention mechanism is designed for data fusion of stock data and news sentiment index as the input of the simple and efficient TrellisNet network for model training and prediction. The performance of our model is systematically evaluated using seven major international stock market indices including S&P500, NYSE, DJI, NASDAQ, FTSE 100, Nikkei 225 and SSE, and comparative experiments demonstrate that SA-TrellisNet is competitive to the other state-of-the-art methods in predicting stock market indices.

Does Investment in Defensive Stocks Act as a Buffer during Market Downturns?

The study examines the performance of defensive stocks during market downturns in the Indian stock market. The research focuses on the period from January 2000 to December 2023. In this study selected stocks from the Fast-Moving Consumer Goods (FMCG) sector (HUL, ITC, Britannia Industries) and Pharma sector (Sun Pharmaceuticals Industries, Dr Reddy Laboratories and Cipla) have been taken into consideration. Five key metrics are covered to assess the stock’s performance: Stock return, Correlation, Beta Compound Annual Growth Rate (CAGR), and Dividend yield. For stock return a comparison is made between stock market return and selected stock return, also the average return of each stock is calculated and compared with the average market return during market downturns. Correlation has been used to understand if there is a relationship between stock returns and nifty returns. Beta has been used to understand the sensitivity of the stock in relation to the market and Compound Annual Growth Rate has been used to analyze the long-term stability of each stock. The average dividend yield is calculated to understand the extra return that an investor can get on his investments on top of capital gain. The study findings revealed that incorporating stocks from the Fast-Moving Consumer Goods (FMCG) and Pharmaceutical (Pharma) sectors protects investors against market downturns, Additionally, the research highlights the long-term stability of returns associated with defensive stocks, making them a valuable component for investors seeking portfolio diversification and a buffer against market fluctuations. Keywords: Defensive Stocks, Market Downturns, Stock Market Return, Beta, CAGR, FMCG, Pharma.

The Relationship between Oil Prices, Gold Prices, the Stock Market, and U.S. GDP

The forecast of GDP has always been a popular research topic today, and its related influencing factors are also complicated. This paper selects three key factors in economic development: gold price, crude oil price, and stock market index, explores their relationship with U.S. GDP, and then uses these three factors as predictive variables to obtain a multiple linear regression equation related to GDP. The research methods of this paper are as follows: Firstly, monthly U.S. GDP, monthly crude oil price, monthly gold price, and monthly S&P 500 index were collected. Secondly, correlation analysis was carried out on these data, including calculation of correlation coefficient and cross-correlation analysis. Correlation analysis showed that GDP had a significant positive correlation with other variables. Then, a multiple linear regression model was established with monthly U.S. GDP as the predicted variable, monthly crude oil price, monthly gold price, and monthly S&P 500 index as the predictor variable. Finally, multiple regression equations are obtained through testing. This multiple regression equation can be used to predict GDP further.

A Study on Novice Investor Behavior in Indian Stock Exchange

Investing is not only a personal financial endeavor but also a critical driver of economic growth, shaping individual prosperity and national development. The research is significant because it emphasizes the critical role that financial literacy plays in influencing investment behavior. It also emphasizes the necessity of ongoing education and awareness campaigns to equip new investors with the information and abilities they need to make wise decisions on the Indian Stock Exchange. All things considered, the results provide insightful information that will help investors, financial institutions, and regulators develop policies to support a more knowledgeable, resilient, and inclusive investment environment in India. This study embarks on a journey to explore the intricate dynamics of investment in India, beginning with an exploration of its foundational role as the engine of economic progress. From influencing aggregate demand to shaping future productive capacity, investments yield significant influence over a nation's economic trajectory, fostering financial markets and enabling companies to raise capital for further growth. In addition, the research reveals that participants have a wide variety of investment objectives, with long-term wealth creation being the primary focus. Investment styles are characterized by a trade-off between risk and return, with moderate strategies being the most common, and considerable shares of aggressive and conservative methods. In emerging markets like India, where investor behaviors vary across a diverse spectrum, understanding the impact of different investor types becomes imperative. Delving into the distinct behaviors of individual, institutional, and foreign investors, this analysis sheds light on their contributions to the Indian stock market, unravelling their unique influences and dynamics.

Forecasting the future: applying Bayesian model averaging for exchange rates drivers in Ghana

ABSTRACT Exchange rate forecasts and stability have been a challenge for developing economies. This study analyses the possible exchange rate predictors in Ghana that are needed for policymaking. We perform an out-of-sample exchange rate forecast for the Ghana Cedis (GHS) per the United States dollar (USD) and the Euro (EUR) and the nominal effective exchange rate (NEER) using the Bayesian model averaging for 1, 3 and 6-month horizons. We observed that domestic predictors largely drive the exchange rates in Ghana with the stock market index and fiscal operations exhibiting the strongest predictive power followed by the Taylor rule, and the real economic sector. Moreover, there is an international spillover from the US economy to Ghana’s exchange rate. Finally, we noticed that forecast accuracy improved at the 3-month-ahead.