Dow Jones Industrial Average Index Research Articles

Galformer: a transformer with generative decoding and a hybrid loss function for multi-step stock market index prediction

The prediction of stock market fluctuations is crucial for decision-making in various financial fields. Deep learning algorithms have demonstrated outstanding performance in stock market index prediction. Recent research has also highlighted the potential of the Transformer model in enhancing prediction accuracy. However, the Transformer faces challenges in multi-step stock market forecasting, including limitations in inference speed for long sequence prediction and the inadequacy of traditional loss functions to capture the characteristics of noisy, nonlinear stock history data. To address these issues, we introduce an innovative transformer-based model with generative decoding and a hybrid loss function, named “Galformer,” tailored for the multi-step prediction of stock market indices. Galformer possesses two distinctive characteristics: (1) a novel generative style decoder that predicts long time-series sequences in a single forward operation, significantly boosting the speed of predicting long sequences; (2) a novel loss function that combines quantitative error and trend accuracy of the predicted results, providing feedback and optimizing the transformer-based model. Experimental results on four typical stock market indices, namely the CSI 300 Index, S&P 500 Index, Dow Jones Industrial Average Index (DJI), and Nasdaq Composite Index (IXIC), affirm that Galformer outperforms other classical methods, effectively optimizing the Transformer model for stock market prediction.

COMPARISON OF VECTOR AUTOREGRESSIVE (VAR) AND VECTOR ERROR CORRECTION MODELS (VECM) FOR THE COMPOSITE STOCK PRICE INDEX (JCI) IN INDONESIA

The capital market has a major role for economic development which is able to encourage capital formation and sustain economic growth. Financial liberalisation between countries in the world will make capital markets in each country interconnected. If there is a large movement in the global stock index, it will also have an impact on the JCI in Indonesia. In addition to global indices, macroeconomics is also seen as capable of influencing capital markets. Inflation and interest rates are macroeconomic components that can be used. The purpose of this research is to be able to see the relationship between one variable and another, namely the Composite Stock Price Index (JCI), Dow Jones Industrial Average (DJIA), inflation rate, interest rate. The VAR/VECM analysis approach was used in this study. Data was taken from 2017 to 2023. The data used for modelling is 84 observations. Based on the results, Inflation and DJIA have a long-term influence on the increase of JCI, while interest rates have no long term influence on JCI in Indonesia. With the model projection for the next 10 years. The projection results state that the variability of JCI in Indonesia is most dominantly explained by the JCI variable itself with a proportion of 95.21619%, while inflation contributes 0.193202%, interest rates 0.249170%, and DJIA index 4.341438%. The results of this analysis can be used as a consideration of strategies in increasing the JCI in Indonesia to maintain the health and stability of the economy.

Analisis Kausalitas Nilai Tukar Rupiah/USD, Dow Jones Industrial Average (DJIA), Nikkei225, Shanghai Index Composite (SSEC) Terhadap Indeks Harga Saham Gabungan (IHSG)

The COVID-19 pandemic has had a negative effect on global stock markets. In addition, the increase in the number of confirmed cases of COVID-19 further increased the volatility of the exchange rate. This study aims to determine whether there is a causal relationship between the Exchange Rate, the Dow Jones Industrial Average (DJIA), the Nikkei225 Index, and the Shanghai Stock Exchange Composite Index (SSEC) and the Composite Stock Price Index (CSPI). The research method used is the Augmented Dickey Fuller Unit Root Test, and Granger Causality. The results of the study using Granger Causality indicate that the Exchange Rate, DJIA Index, and Nikkei225 Index have a causal relationship to the Composite Stock Price Index. The SSEC index does not have a causal relationship with the Composite Stock Price Index. This means that the JCI movement has the influence of the Exchange Rate, the DJIA Index, and the Nikkei225 Index.

COMPARATIVE ANALYSIS OF THE US STOCK MARKET PERFORMANCE: PRE AND POST COVID-19

The financial markets have been significantly impacted by the global COVID-19 epidemic due to international lockdowns and decreased demand, which has further exacerbated the economic situation, including a decline in crude oil prices. The country has experienced economic consequences as a result of the coronavirus and subsequent lockdown measures. The Dow Jones Industrial Average (DJIA) is a price-weighted stock market index that tracks 30 major companies listed on US stock exchanges. While the DJIA is a widely recognized equity index, some professionals argue that it may not provide a comprehensive representation of the overall US stock market compared to larger indices such as the S&P 500 or Russell 2000 Index. This study aims to assess the impact of the stock market shutdown caused by COVID-19. Specifically, it investigates the extent to which the lockdown measures have affected the US stock market and whether there are any differences in market reactions before and after the onset of the pandemic. By comparing the performance of the DJIA index before and after COVID-19, the study aims to uncover the level of influence experienced.

A novel recurrent neural network based online portfolio analysis for high frequency trading

The Markowitz model, a Nobel Prize winning model for portfolio analysis, paves the theoretical foundation in finance for modern investment. However, it remains a challenging problem in the high frequency trading (HFT) era to find a more time efficient solution for portfolio analysis, especially when considering circumstances with the dynamic fluctuation of stock prices and the desire to pursue contradictory objectives for less risk but more return. In this paper, we establish a recurrent neural network model to address this challenging problem in runtime. Rigorous theoretical analysis on the convergence and the optimality of portfolio optimization are presented. Numerical experiments are conducted based on real data from Dow Jones Industrial Average (DJIA) components and the results reveal that the proposed solution is superior to DJIA index in terms of higher investment returns and lower risks.

Analysis of global stock index, inflation and interest rates on the Indonesia Stock Exchange joint stock price index

This study examines the influence of global stock indices and macroeconomic conditions in Indonesia on the Jakarta Composite Index (JCI). The global stock indices analyzed in this study are the Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Shanghai Stock Exchange Composite (SSE), Strait Singapore index. The macroeconomic indicators that analyzed in this study are the inflation rate, the US dollar exchange rate against the Indonesian rupiah, and the BI rate. This study was conducted using secondary data. The research period was 3 years for 36 months from January 2018 to December 2020. The data used in this study are secondary data with a sample collection method using Purposive Sampling against JCI. Data analysis in this study was carried out using Microsoft Office Excel 2016, as well as hypothesis testing using panel data regression analysis with the Eviews program version 10.0 with a significance level of 5%. Research Results The results showed that in the short term, Index DJIA, Nikkei Index 225, Interest rates positively affect the movement of JCI. This suggests that an increase in the DJIA index, Nikkei 225 Index, and interest rates will result in an increase in the JCI, While Shanghai Index, STI, Inflation does not affect the JCI.

Changes in the DJIA: market reactions and economic cycles

PurposeThe purpose of this study is to explain the mixed results to changes in the DJIA index documented in the literature. The authors show that economic cycles, especially recessionary periods, explain the difference in findings.Design/methodology/approachThe authors examine changes in the Dow Jones Industrial Average (DJIA) from 1929 to 2019 to evaluate immediate and long-term market reactions after a component change. Using multiple event-study methodologies, the authors examine the full era, the pre- and post-exchange traded fund (ETF) windows and economic cycles using both pre and post-estimation windows.FindingsIn aggregate, DJIA additions do not present an increase in wealth; however, wealth effects are positive during expansions and negative during recessions. Deletions have a negative wealth effect. The authors find weak evidence of an indexing effect. Additions are positive post-1998, and deletions remain negative regardless of era. In the long run, firms added to the DJIA have positive abnormal returns in the second year after inclusion. Deletions in recessionary times have negative returns three years after removal, a signal of longer-term wealth decline for these firms.Research limitations/implicationsThe DJIA changes periodically to better represent industries relevant to the blue-chip market, and the findings have implications for fund managers and active investors.Practical implicationsThe DJIA changes periodically to better represent industries relevant to the blue-chip market, and the findings have implications for fund managers and active investors.Originality/valuePrior literature presents limited time series of data points and mixed results and implications. The authors find that the economic cycle is a driving factor that supports predicted signs and amounts of wealth change. Furthermore, the authors see limited ETF impact on DJIA changes and some impact of the choice of estimation period.

Decision Analysis on the Financial Performance of Companies Using Integrated Entropy-Fuzzy TOPSIS Model

Sustainable economic development plans have been shattered by the devastating COVID-19 crisis, which brought about an economic recession. The companies are suffering from financial losses, leading to financial distress and disengagement from sustainable economic goals. Many companies fail to achieve considerable financial performances, which may lead to unachieved organizational goal and a loss of direction in decision-making and investment. According to the past studies, there has been no comprehensive study done on the financial performance of the companies based on liquidity, solvency, efficiency, and profitability ratios by integrating the entropy method and fuzzy technique for order reference based on similarity to the ideal solution (TOPSIS) model in portfolio investment. Therefore, this paper aims to propose a multi-criteria decision-making (MCDM) model, namely the entropy-fuzzy TOPSIS model, to evaluate the financial performances of companies based on these important financial ratios for portfolio investment. The fuzzy concept helps reduce vagueness and strengthen the meaningful information extracted from the financial ratios. The proposed model is illustrated using the financial ratios of companies in the Dow Jones Industrial Average (DJIA). The results show that return on equity and debt-to-equity ratios are the most influential financial ratios for the performance evaluation of the companies. The companies with good financial performance, such as the best HD company, have been determined based on the proposed model for portfolio selection. A mean-variance (MV) model is used to validate the proposed model in the portfolio investment. At a minimum level of risk, the proposed model is able to generate a higher mean return than the benchmark DJIA index. This paper is significant as it helps to evaluate the financial performance of the companies and select the well-performing companies with the proposed model for portfolio investment.

Global gold prices forecasting using Bayesian nonparametric quantile generalized additive model

Gold is one of the most attractive commodities and popular investments. Investment experts often recommend investing in gold because gold is one of the safest investments. It is a stable classic hedge, although the conditions of currency volatility or global markets are depreciated. However, the gold price fluctuations can be influenced by some other factors, such as the USD Index, which reflect and measure the strength of the US Dollar currency, and the Index of Dow Jones Industrial Average (DJIA) or a reflection of the political and economic conditions of the stock market. In this study, we conduct a global gold price forecast (USD) based on the USD Index, the DJIA Index, and the influence of time trends. Based on the data's characteristics, we face the fact that the data is nonlinear, contains outliers, and its pattern is not easy to specify parametrically. Due to the complexity of the model, we then propose a more flexible, robust modeling technique called the Bayesian Nonparametric Quantile Generalized Additive Model method. According to the results for the median case, the proposed method shows an accurate forecasting category due to the value of the Mean Absolute Percentage Error, MAPE less than 10 percent.

Financial Network Analysis on the Performance of Companies Using Integrated Entropy-DEMATEL-TOPSIS Model.

In this paper, we propose a multi-criteria decision making (MCDM) model by integrating the entropy–DEMATEL with TOPSIS model to analyze the causal relationship of financial ratios towards the financial performance of the companies. The proposed model is illustrated using the financial data of the companies of Dow Jones Industrial Average (DJIA). The financial network analysis using entropy–DEMATEL shows that the financial ratios such as debt to equity ratio (DER) and return on equity (ROE) are classified into the cause criteria group, whereas current ratio (CR), earnings per share (EPS), return on asset (ROA) and debt to assets ratio (DAR) are categorized into the effect criteria group. The top three most influential financial ratios are ROE, CR and DER. The significance of this paper is to determine the causal relationship of financial network towards the financial performance of the companies with the proposed entropy–DEMATEL–TOPSIS model. The ranking identification of the companies in this study is beneficial to the investors to select the companies with good performance in portfolio investment. The proposed model has been applied and validated in the portfolio investment using a mean-variance model based on the selection of companies with good performance. The results show that the proposed model is able to generate higher mean return than the benchmark DJIA index at minimum risk. However, short sale is not allowed for the applicability of the proposed model in portfolio investment.

Pengaruh Indeks Dow Jones Industrial Average (DJIA), Tingkat Inflasi, Bi 7-Day Repo Rate, dan Kurs USD/IDR Terhadap Indeks Saham Syariah Indonesia (ISSI) di Bursa Efek Indonesia (BEI)

Indonesia is one of the largest Muslim countries in the world that makes sharia investments in the capital market with efforts to develop the Islamic financial sector. Several factors that influence stock price fluctuations include global indices and macroeconomic variables. This study aims to determine the simultaneous and partial effect of the Dow Jones Industrial Average (DJIA), Inflation Rate, BI 7-Day Repo Rate, and USD/IDR Exchange Rate on the Indonesian Sharia Stock Index (ISSI). This study uses secondary data in the form of monthly index summary reports obtained from financial websites. The object of research is the Indonesian Sharia Stock Index. This study has a population of 60 data with the determination of the sample using saturated samples. The data analysis technique used multiple linear regression analysis techniques. Hypothesis testing using the F test and T test. The results of the simultaneous test analysis (Test F) show that the variables of the Dow Jones Industrial Average (DJIA) (X1), Inflation Rate (X2), BI 7-Day Repo Rate (X3) and USD/IDR Exchange Rate (X4) has a significant influence on the Indonesian Sharia Stock Index (ISSI) on the Indonesia Stock Exchange (IDX) Period August 2016 – July 2021. Partially, the DJIA Index and the Inflation Rate have a positive and significant effect on Indonesian Sharia Stock Index. Meanwhile, the BI 7-Day Repo Rate and the USD/IDR Exchange Rate have a negative and significant effect on Indonesian Sharia Stock Index.

Exchange Rate and Stock Markets During Trade Conflicts in the USA, China, and India

During trade tussles, most of the stock exchanges are impacted, both directly and indirectly, thereby influencing the exchange rates of the countries. This article gives a comprehensive insight into the effects of exchange rate on the USA, China, and Indian stock market, that is, the Dow Jones industrial average index (DJI), Shanghai Stock Exchange (SSE) composite index, and Nifty50 index during trade conflicts. Trade conflicts here talk about the recent trade war between the USA and China, which was invariably fallen in line with the outbreak of COVID-19. Sample of this study comprises the daily closing price from different indices and exchange rate values US dollar, yuan, and rupee. This article uses ordinary least square (OLS) model and generalized autoregressive conditional heteroskedasticity (GARCH) model to analyze the volatility and influence of exchange rate over the stock market. Results from OLS model indicated that the fluctuations in exchange rate have minimum impact on the daily closing price of stock indices, that is, DJI, SSE, and Nifty50 in the USA, China, and India, respectively, however, exchange rate impacts volume of shares traded in all three stock exchanges. The GARCH model implies that the conditional variance is less volatile for Nifty volume, but highly volatile for SSE and DJI stock volumes traded. The findings of the study provide insights to the domestic and foreign investors regarding their investment in the stock market during trade conflicts and mostly during COVID-19 situation.

Series Hybridization of Parallel (SHOP) models for time series forecasting

Accurate forecasting of real-world systems becomes a highly challenging task due to the inherent complexity of time series modeling. Hybrid models have been successfully applied to deal with such problems and yield desired forecasting accuracy. The fundamental objective of hybridization is to exploit the unit modeling benefits of every single model and lift its disadvantages. For reaching these goals, individual models are combined in two main parallel and series frameworks. The parallel hybridization method relied on employing different individual models and integrated the weighted forecasts to capture the advantages contained in all models, concurrently. However, existing parallel hybrid models suffer from some crucial shortcomings that need to be addressed and eliminated. One of the critical deficiencies of parallel models is that the residual obtained by different models is not modeled, and the unprocessed patterns have remained in the data. The principal goal of this paper is to alleviate this deficiency of parallel hybrid models using the capability of the series hybridization strategy in modeling remaining patterns in residuals. Thus, the key innovation of this study is to combine parallel hybrid models employing a series hybridization scheme to yield an enhanced forecasting model and overcome the drawback of the parallel models. Despite the vast hybrid models proposed for combining individual models, this paper aims to combine both the above-mentioned hybrid structures instead of individual models. For this purpose, the novel hybrid model named Series Hybridization of Parallel (SHOP) model is proposed, which integrates a parallel hybrid model by series hybridization approach. In this research, Autoregressive Integrated Moving Average (ARIMA) and Multilayer perceptrons (MLP) models are used to implement the proposed hybrid SHOP structure. In this way, the SHOP contains a series hybridization of parallel hybridization of ARIMA and MLP models. The effectiveness of the SHOP model is verified by applying it to four benchmark data sets, including the closing of the DAX index, the closing of the Nikkei 225 index (N225), the opening of the Dow Jones Industrial Average Index (DJIAI), and the wind speed data in Colorado State. The predictive power of the SHOP model is evaluated by comparing the obtained results with ARIMA, MLP, LSTM, RBFNN, SVM, and traditional series and parallel hybridization of ARIMA and MLP models. Remarkably, the obtained forecasting accuracy from the SHOP model is outstanding than other models.

A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting

Hybridization of individual models emerged as a predominant alternative for increasing accuracy in time series forecasting. The literature is abundant on providing hybrid methods aiming at improving forecasting accuracy and comprehensive pattern recognition. The principle behind all hybrid models' success is improving forecasting accuracy. One of the most widely used combination methods in line with the common objective of the hybridization concept is a parallel approach in which the forecasting model applied on the original time series and the weighted forecasts generate the final hybrid result. In parallel hybridization, the key is how to select the weights of each model. The weighting algorithm plays a significant role and the degree of accuracy in such models directly depends on it. However, the traditional parallel hybrid models developed to enhance forecasting accuracy employing multiple individual models simultaneously, faced some limitations, e.g., missing considering the reliability and generalization criteria of developed hybrid models. From the literature, it is observed that none of the parallel hybrid modes proposed in the literature focused on the improving reliability of the hybrid model to obtain better model generalization for unseen data. Thus, the main objective of this study is to propose a novel class of hybrid models named reliability-based parallel hybridization (RPH). The main goal of RPH methodology is to improve hybrid models' reliability rather than accuracy using a new reliable-based weighting algorithm (RWA). The RWA instead of traditional accuracy-based weighting algorithms in which error measurements are minimized, the reliability of hybrid models is maximized to determine the exact optimum weights of individual models. The RWA computed the optimum weights of forecasts regarding minimizing performance changing of the hybrid model in the validation data set. The RPH model proposed two crucial concepts for the first time: (1) Bringing up reliability in hybridization procedure (2) Proposing a novel reliable-based weighting algorithm to maximize the generalization power of the hybrid model. The bi-component version of the RPH model is proposed in this paper using ARIMA and MLP models. The forecasting power of the proposed PRH constructed based on ARIMA and MLP models is verified using the benchmark data sets e.g. the closing of Standard and Poor's 500 indexes (S&P500), the closing of the Shenzhen Integrated Index (SZII), and the opening of the Dow Jones Industrial Average Index (DJIAI). The experimental results indicate that the forecasting performance of the RPH model is much better than traditional accuracy-based parallel hybrid models.

Correcting for risk premium on Extended Generalised Leland Models: an empirical study on Dow Jones Industrial Average (DJIA) index options

The relative option pricing performance of Extended Generalised Leland models is examined in this study. We generalise the extended Leland models based on the implied adjusted volatility introduced in the models. Non-parametric framework is fitted into parametric option-pricing framework based on the Leland models to achieve a more realistic option pricing. To reflect the real probability measure, the implied adjusted information is corrected in terms of risk premium. This study concentrates mainly in examining the option-implied information produced by the models after correcting for risk-premium. Data extracted from DJIA index options are employed in this study, which covers the period from January 2009 until the end of 2015. We discovered that the option-implied volatility, which is priced using the Extended Generalised Leland models, especially after being corrected for risk-premium factor improves the option valuation accuracy significantly.

Economic Policy Uncertainty and Stock Markets: A Multifractal Cross-Correlations Analysis

We utilize the multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the cross-correlations between the US economic policy uncertainty (EPU) and US stock markets in the framework of Fractal Market Hypothesis (FMH). The data contain daily closing values of EPU, and the returns of Dow Jones Industrial Average Index (DJI), S&P 500 index (GSPC) and NASDAQ Composite Index (IXIC). Our empirical results show that changes in EPU and fluctuations in the US stock markets interact in a nonlinear way. Furthermore, there exists significant multifractality in the cross-correlations between EPU and stock markets. The cross-correlations exhibit dynamics and are affected by major international events. We capture the underlying mechanisms such as multifractality and nonlinear relation that dominate EPU-US stock markets nexus by means of FMH. The findings add a new dimension to the existing literature, and are important for market participants to adjust investment decisions.

Herding Behavior and Cryptocurrency: Market Asymmetries, Inter-Dependency and Intra-Dependency

The study investigates herding behavior in cryptocurrencies in different situations. This study employs daily returns of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018. Quantile regression method is employed to test the herding effect in market asymmetries, inter-dependency and intra-dependency cases. Findings confirm the presence of herding in cryptocurrency in upper quantiles in bullish and high volatility periods because of overexcitement among investors, which lead to high volume trading. Major cryptocurrencies cause herding in sub-major cryptocurrencies, but it is a unidirectional relation. However, no intra-dependency effect among cryptocurrencies and equity market is observed. Results indicate that in the CKK model herding exists at upper quantile in market that may be due when the market is moving fast, continuously trading, and bullish trend are prevailing. Further analysis confirms this narrative as, at upper quantile, the beta of bullish regime is negative and significant, meaning the main source of market herding is a bullish trend in investment, which increases market turbulence and gives investors opportunity to herd. Also, we found that herding in cryptocurrencies exits in high volatility periods, but this herding mostly depends on market activity, not market movement.

Factors Affecting the Indonesia Stock Exchange: A Multi-Index Approach

This study examines the influence of world and regional capital market conditions on the Indonesian capital market (Indonesia Stock Exchange) condition. The DJIA (Dow Jones Industrial Average) index was used as a representative of the international capital market while the Hang Seng index and the Nikkei 225 index were used as a representative of regional capital market conditions. These two indices were chosen because the Japanese capital market was one of the most advanced capital markets in the world and the Hong Kong capital market, although not as big as Japan, still played an important role in the world. The data were obtained from Yahoo Finance during the period of 2014-2018. The dependent variable was the change in the JCI (Jakarta Composite Index), while the independent variables were changes in the index of DJIA, Nikkei 225 and Hang Seng index. Using daily data analyzed by the ARIMA method (1,1), it was found that there was a significant positive effect of DJIA with lag 1 and Hang Seng index on the JCI, but no significant effect was found from the Nikkei 225 index on the JCI.

Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting

This paper proposes a hybrid modelling approach for forecasting returns and volatilities of the stock market. The model, called ARFIMA-WLLWNN model, integrates the advantages of the ARFIMA model, the wavelet decomposition technique (namely, the discrete MODWT with Daubechies least asymmetric wavelet filter) and artificial neural network (namely, the LLWNN neural network). The model develops through a two-phase approach. In phase one, a wavelet decomposition improves the forecasting accuracy of the LLWNN neural network, resulting in the Wavelet Local Linear Wavelet Neural Network (WLLWNN) model. The Back Propagation (BP) and Particle Swarm Optimization (PSO) learning algorithms optimize the WLLWNN structure. In phase two, the residuals of an ARFIMA model of the conditional mean become the input to the WLLWNN model. The hybrid ARFIMA-WLLWNN model is evaluated using daily closing prices for the Dow Jones Industrial Average (DJIA) index over 01/01/2010 to 02/11/2020. The experimental results indicate that the PSO-optimized version of the hybrid ARFIMA-WLLWNN outperforms the LLWNN, WLLWNN, ARFIMA-LLWNN, and the ARFIMA-HYAPARCH models and provides more accurate out-of-sample forecasts over validation horizons of one, five and twenty-two days.

Diffusion Entropy Analysis: Stability of Indian Stock Market

The large amount of available data of stock markets becomes very beneficial when it is transformed to valuable information. The analysis of this huge data is essential to extract out the useful information. In the present work, we employ the method of diffusion entropy to study time series of different indexes of Indian stock market. We analyze the stability of Nifty50 index of National Stock Exchange (NSE) India and SENSEX index of Bombay Stock Exchange (BSE), India in the vicinity of global financial crisis of 2008. We also apply the technique of diffusion entropy to analyze the stability of Dow Jones Industrial Average (DJIA) index of USA. We compare the results of Indian Stock market with the USA stock market (DJIA index). We conduct an empirical analysis of the stability of Nifty50, Sensex and DJIA indexes. We find significant drop in the value of diffusion entropy of Nifty50, Sensex and DJIA during the period of crisis. Both Indian and USA stock markets show bull market effects in the pre-crisis and post-crisis periods and bear market effect during the period of crisis. Our findings reveal that diffusion entropy technique can replicate the price fluctuations as well as critical events of the stock market.