SP500 Index Research Articles

The Impact of ESG Ratings on Investment Decisions in the Energy Sector During Uncertain Times

With increasing global awareness of sustainability, the financial sector must inevitably adapt. Our research investigates the role of Environmental, Social, and Governance (ESG) ratings in investment decision-making, particularly in the energy sector, by examining their impact on portfolio performance. The study aims to assess the impact of ESG integration on portfolio risk and returns. Using data from the SP500 index, econometric models and Monte Carlo simulations are employed to evaluate how ESG considerations influence portfolio optimization and risk management. The study reveals that integrating ESG criteria into investment strategies, especially during market volatility, may not always directly affect returns, they provide significant benefits in terms of risk mitigation and ethical investment alignment.

Analysis and Forecasting the Price of the S&P 500 Index Using the Arima Model

The results of the research allowed to determine that the chosen S&P 500 index can serve as a reflection of the state and forecasts of economic development of the United States. A successful forecast of the index can serve not only as a key point in building an individual investment strategy, but also as an indicator of the general state of the economy. The mathematical model for predicting the dynamics of the index was built. Through exploratory data analysis, a better understanding of the time series and its characteristics was obtained. The application of various statistical methods, such as moving statistics and stationarity tests, made it possible to identify trends and seasonality in the data. Seasonal decomposition and logarithmic transformation helped to better understand the contribution of each component to the overall index dynamics, and special attention was paid to the stationary ADF test, where was considered not only the code but also the significant formulas. The optimal selection of parameters was done automatically. ARIMA model showed good results – the evaluation of the model accuracy included the comparison of the predicted values with the actual values of the SP500 index, both visually and using several metrics – MAE, MSE, RMSE, MAPE. The result of the work is a model for predicting the dynamics of the S & P 500 index, implemented using the Python programming language with a MAPE of about 1.9%, the accuracy of the model is 98.1%. and such good results indicate the possibility of using this tool by market participants in real conditions.

Portfolio Based on AR Model Using 5 Stocks

An investment portfolio is a collection of stocks, bonds, and financial derivatives held by investors or financial institutions, aimed at diversifying risks and maximizing returns. This article selects stocks of Manulife, BOA, Apple, PLC, and National Pharmaceutical Group from October 21, 2022 to August 9, 2023. This article uses AR model, minimum variance model, and maximum Sharpe's ratio model to predict stock prices, and establishes investment portfolios based on the predicted values. Firstly, based on the first 75% of the data, this article predicts the last 25% of the data. Secondly, based on the prediction, the minimum variance model and the maximum Sharpe ratio model are applied and compared with SP500 within the same time frame. However, the results indicate that the SP500 index performs best in terms of yield. This study suggests that purchasing market index funds is an important investment decision for investors during the volatile US dollar interest rate hike cycle.

Enhancing Portfolio Allocation by LSTM Model

Accurate forecasting of stock prices and the construction of optimized portfolios are essential for investors in the dynamic technology sector. This paper proposes a comprehensive approach that combines Long Short-Term Memory (LSTM) models with portfolio construction techniques specifically tailored to the stocks of Apple Inc. (AAPL), Meta Platforms Inc. (META), Amazon.com Inc. (AMZN), Microsoft Corporation (MSFT), and NVIDIA Corporation (NVDA) from January 1st ,2018 to May 31st, 2023. By leveraging the sequential nature of historical stock price data(80% of data), LSTM models capture complex patterns and dependencies, enabling more precise predictions of Adjusted Close (Adj Close) prices. Subsequently, the forecasted prices (20% of data)are utilized to construct optimized portfolios that maximize returns and minimize risks within the technology sector using Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics. The overall result of the prediction data is similar to the actual data which implies that the integration of LSTM-based forecasting and portfolio construction provides a robust framework for informed investment decision-making and risk management. And the application of Monte Carlo simulations, efficient frontier analysis, and key risk-return metrics gave us two portfolio allocation options : Minimum Variance model (40% of AAPL,60% of MSFT) and Maximum Sharpe Ratio model ( 47% of META, 53% of NVDA). The evaluation of the two portfolios show that the strategy can significantly beat the SP500 index.

Network connectedness and portfolio hedging of green bonds, stock markets and commodities

PurposeThis study aims to investigate the impact of the COVID-19 pandemic on the time-frequency connectedness between green bonds, stock markets and commodities (Brent and Gold), with a particular focus on China and its implication for portfolio diversification across different frequencies.Design/methodology/approachTo this end, the authors implement the frequency connectedness approach of Barunik and Krehlik (2018), followed by the network connectedness before and during the COVID-19 outbreak. In particular, the authors implement more involvement in portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness for green bonds and other financial assets.FindingsThe time-frequency domain spillover results show that gold is the net transmitter of shocks to green bonds in the long run, whereas green Bonds are the net recipients of shocks, irrespective of time horizons. The subsample analysis for the pandemic crisis period shows that green bonds dominate the network connectedness dynamic, mainly because it is strongly connected with the SP500 index and China (SSE). Thus, green bonds may serve as a potential diversifier asset at different time horizons. Likewise, the authors empirically confirm that green bonds have sizeable diversification benefits and hedges for investors towards stock markets and commodity stock pairs before and during the COVID-19 outbreak for both the short and long term. Gold only offers diversification gains in the long run, while Brent does not provide the desired diversification gains. Thus, the study highlights that green bonds are only an effective diversified.Originality/valueThis study contributes to the existing literature by improving the understanding of the interconnectedness and hedging opportunities in short- and long-term horizons between green bonds, commodities and equity markets during the COVID-19 pandemic shock, with a particular focus on China. This study's findings provide more implications regarding portfolio allocation and risk management by estimating hedge ratios and hedging effectiveness.

Impacts of U.S. Stock Market Crash on South African Top Sector Indices, Volatility, and Market Linkages: Evidence of Copula-Based BEKK-GARCH Models

This paper examines the effects of the Standard and Poor’s 500 (SP500) stock index crash during the global financial crisis and the COVID-19 pandemic periods on the South African top sector indices (basic materials, consumer goods, consumer services, financials, healthcare, industrials, technology, and telecommunication). The results of a copula-based BEKK-GARCH approach technique demonstrate the existence of price and volatility spillover during times of stock crashes. We discover that during a stock crisis, strong shocks and higher volatility spillover effects from the United States (U.S.) SP500 index to the top sector indices of the South African Johannesburg Stock Exchange (JSE) markets are more significant. However, there is no integrated economy, as the results did not show any spillover effects from South Africa to U.S. markets. Furthermore, the Gumbel copulas have higher dependence parameters, implying that extreme co-movements occur in the upper tails, suggesting the possibility of a large transmission of shocks from the SP500 to the eight top sector indices of the JSE and showing an asymmetric dependence between these markets. This result is important for investors willing to invest in the South African sector of equity markets to develop hedging strategies to prevent risk spillover from developed markets.

Spillovers from stock markets to currency markets: Evidence from Copula-CoVar with time-varying higher moments

ABSTRACT This study analyses the relationship between equites and foreign exchange markets by employing a conditional value at risk (CoVaR) framework for developed and developing economies accounting for an upside or downside shock, greater or equal to their VaRs. The CoVaR of currency returns conditional on both, the local equity and SP500 index are significant and greater than their unconditional VaRs for most currencies. The ∆CoVaRs for currency market conditional on the local stock index are greater than those conditional on the SP500, showing that local stock index provides greater contributions to the shocks in currency returns compared to the SP500 index. Our findings have important implications for developing cross-market and cross-border hedging strategies.

Keeping up with the CSR Joneses: The impact of industry peers on focal firms’ CSR performance

Using US-listed firms from 2000 to 2015 as a sample, we report a strong enhancing effect of industry peers’ corporate social responsibility (CSR) performance on focal firms’ CSR performance. Firms improve their CSR performance by ~10.15% standard deviation when other peer firms in the same industry increase their CSR by one standard deviation. Moreover, this peer effect is more substantial when the peer firms are closer to focal firms, are included in SP500 index, and have a relatively larger size than focal firms. Focal firms are less likely to follow industry peers if they are industry leaders, have stronger earnings capacity, occupy foreign business and receive fewer analyst followings. Finally, firms following industry peers’ CSR policies achieve less product market shares and poorer performance, but a higher firm value. We suggest that executives should not blindly follow peer firms’ suit and strategically invest in CSR to achieve higher competitiveness.

Financial crisis in the framework of non-zero temperature balance theory.

In financial crises, assets see a deep loss of value, and the financial markets experience liquidity shortages. Although they are not uncommon, they may cause by multiple contributing factors which makes them hard to study. To discover features of the financial network, the pairwise interaction of stocks has been considered in many pieces of research, but the existence of the strong correlation between stocks and their collective behavior in crisis made us address higher-order interactions. Hence, in this study, we investigate financial networks by triplet interaction in the framework of balance theory. Due to detecting the contribution of higher-order interactions in understanding the complex behavior of stocks we take the advantage of the order parameter of the higher-order interactions. Looking at real data of the financial market obtained from S&P500 index(SPX) through the lens of balance theory for the quest of network structure in different periods (on and off-crisis) faces us with the existence of a structural difference of networks corresponding to the periods. Addressing two well-known crises the Great regression (2008) and the Covid-19 recession (2020), our results show an ordered structure forms in the on-crisis period in the financial network while stocks behave independently far from a crisis. The formation of the ordered structure of stocks in crisis makes the network more resilient to disorder (thermal fluctuations). The resistance of the ordered structure against applying the disorder measure the crisis strength and determine the temperature at which the network transits. There is a critical temperature, Tc, in the language of statistical mechanics and mean-field approach which above, the ordered structure destroys abruptly and a first-order phase transition occurs. The stronger the crisis, the higher the critical temperature.

The Impact of Russia -Ukraine War on The U.S. Stock Market–Based on Event Study Methodology

Since Russia is important energy exporter in the world, the conflict between Russia and Ukraine became a world focus nowadays. According to former researchers’ works, we choose the event analysis method to gain an insight into the typical effect on energy sector caused by this conflict and give two hypotheses. Firstly, we propose that the event would have a negative shock on the energy career showed with volatilities of index. And then we present that the fungibility of energy industry remains the stability of returns of index and stocks. By extracting returns and volatilities of SP500 index and returns of the Great consumption index of China from Wind database, also filtrating the famous energy sector U.S. stocks based on the list in the Eastern Wealth, we prove the assumptions with examining the significance of statistics. Using the event analysis method to examine the difference of before and after the event, we get the results which indicate the significances of volatilities of SP500 index. But the returns of SP500 index, the Great consumption index of China, and U.S. stocks do not show the significance differences. Contents above prove two assumptions we mentioned before. It is honorable for us that this research might be practical to offer advice and give a little guidance for market investors.

GARCH option pricing with volatility derivatives

This paper studies benefits of joint estimations for GARCH option pricing that utilize both stock returns and volatility derivatives. The proposed estimations not only provide realistic volatility term structures but also generate flat skewness term structures much like those seen in the S&P 500 index (SPX) options data. In particular, the estimated GARCH models yield a highly persistent volatility component, which allows the leverage effect to hold up until long horizons. Such a persistent volatility component is key to modeling long-term tail risk and pricing long-term put options. Overall, our exercise highlights the usefulness of volatility derivatives in GARCH option valuation.

US Stocks Market Movements Prediction: Classification of SP-500 Using Machine Learning Technology

In the field of quantified investment, risk quantification and maximum expected return are the problems focused on by the investors. Besides, a powerful toolkit for predicting the stock price movement is also very important for investors. In this paper, five stocks that are components of the SP-500 Index are selected, and the Mean-Variance method is used to optimize the portfolio of the above stocks. Moreover, five machine learning methods are compared to evaluate the performance in the application of stock price movement prediction. The results show that the combination of “AMZN”, “MSFT” and “AAPL” can achieve a good expected return within a low risk. In addition, the Artificial Neural Network method has the highest accuracy in predicting the multiclass stock price movement. Our research has a reference significance for the investors in the application of risk quantification and stock price prediction.

Testing for the Presence of the Leverage Effect without Estimation

Problem: The leverage effect plays an important role in finance. However, the statistical test for the presence of the leverage effect is still lacking study. Approach: In this paper, by using high frequency data, we propose a novel procedure to test if the driving Brownian motion of an Ito^ semi-martingale is correlated to its volatility (referred to as the leverage effect in financial econometrics) over a long time period. The asymptotic setting is based on observations within a long time interval with the mesh of the observation grid shrinking to zero. We construct a test statistic via forming a sequence of Studentized statistics whose distributions are asymptotically normal over blocks of a fixed time span, and then collect the sequence based on the whole data set of a long time span. Result: The asymptotic behaviour of the Studentized statistics was obtained from the cubic variation of the underlying semi-martingale and the asymptotic distribution of the proposed test statistic under the null hypothesis that the leverage effect is absent was established, and we also show that the test has an asymptotic power of one against the alternative hypothesis that the leverage effect is present. Implications: We conducted extensive simulation studies to assess the finite sample performance of the test statistics, and the results show a satisfactory performance for the test. Finally, we implemented the proposed test procedure to a dataset of the SP500 index. We see that the null hypothesis of the absence of the leverage effect is rejected for most of the time period. Therefore, this provides a strong evidence that the leverage effect is a necessary ingredient in modelling high-frequency data.

Bayesian estimation for the Tukey GH distribution with an application

Tukey GH is a transformed normal distribution, having two parameters (g,h). The g parameter represents skew measuring, and the h represents kurtosis measuring. Our motivation is to affect these parameters on the behavior for a simulation data and real data, such as the Iraqi stock market Index (ISX60) and the Standard and Poor’s (SP500) index using the Bayesian framework. Then, our aim is to find the estimation for the parameters in this distribution using empirical and parametric Bayesian methods, and study the effective on the simulation and real data. The simulation study will be shown the behavior of the parameters with a different number of sampling and prior distributions. We will use the real data from ISX60 and SP500 index.

Sentimen Investor, Faktor Fundamental Makroekonomi dan Excess Return Pasar Saham di Indonesia

This study aims to examine the effect of investor sentimen and macroeconomic fundamentals on excess returns which are useful for decision making in optimizing portfolios. This study used the aggregate market with the aim of knowing the entire stock market in Indonesia, and used daily data during 2020 with a total of 242 data according to the number of trading days in 2020. This study used trading volume, advance-decline ratio, market turnover, share turnover for sentiment investor indicators. SBI Interest Rate, SP500 Index, and exchange rate were used as indicators of macroeconomic fundamentals. The sentiment investor proxies are analyzed using Principal Component Analysis (PCA). Through GARCH analysis, it is found that investor sentimen has a positive effect on excess return. Based on the Granger Causal test, the study found that excess return granger cause investor sentiment, but not vice versa. Regarding macroeconomic factors, it can be concluded that macroeconomic variables, namely interest rates, the US SP500 index, and exchange rate have a simultaneous effect on excess return, where the macroeconomic variables explain 22,64% excess return in the Indonesian stock market.

Mark-to-Market (or Wealth) Taxation in the U.S.: Evidence from Options

Recent U.S. tax proposals under various names (e.g., wealth taxes, estate tax reform, etc.) center on mark-to-market (MTM) taxation, which eliminates investors’ ability to defer or avoid capital gains taxes. To provide insight on potential effects of these tax proposals, we exploit a unique U.S. setting where “index” options on the S&P 500 Index (SPX) face MTM taxation whereas nearly identical “non-index” options on the exchange traded fund (ETF) tracking the S&P 500 Index (SPY) do not. We find new evidence of asset price consequences to MTM taxation, suggesting that MTM taxation depresses asset prices as investors appear to avoid assets subject to MTM near year-end. Additional analysis suggests this result is driven by tax, rather than administrative, costs of MTM. From a policy perspective, this suggests that 1) MTM taxation has negative, unintended market consequences in the U.S. and 2) U.S. investors will engage in actions to avoid MTM taxation. Both attributes caution policymakers in any attempts to broaden MTM taxation.

The Application of Stock Index Price Prediction with Neural Network

Stock index price prediction is prevalent in both academic and economic fields. The index price is hard to forecast due to its uncertain noise. With the development of computer science, neural networks are applied in kinds of industrial fields. In this paper, we introduce four different methods in machine learning including three typical machine learning models: Multilayer Perceptron (MLP), Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) and one attention-based neural network. The main task is to predict the next day’s index price according to the historical data. The dataset consists of the SP500 index, CSI300 index and Nikkei225 index from three different financial markets representing the most developed market, the less developed market and the developing market respectively. Seven variables are chosen as the inputs containing the daily trading data, technical indicators and macroeconomic variables. The results show that the attention-based model has the best performance among the alternative models. Furthermore, all the introduced models have better accuracy in the developed financial market than developing ones.

Inversion of convex ordering in the VIX market

We investigate conditions for the existence of a continuous model on the S&P 500 index (SPX) that jointly calibrates to a full surface of SPX implied volatilities and to the VIX smiles. We present a novel approach based on the SPX smile calibration condition . In the limiting case of instantaneous VIX, a novel application of martingale transport to finance shows that such model exists if and only if, for each time t, the local variance is smaller than the instantaneous variance in convex order. The real case of a 30-day VIX is more involved, as averaging over 30 days and projecting onto a filtration can undo convex ordering. We show that in usual market conditions, and for reasonable smile extrapolations, the distribution of in the market local volatility model is larger than the market-implied distribution of in convex order for short maturities T, and that the two distributions are not rankable in convex order for intermediate maturities. In particular, a necessary condition for continuous models to jointly calibrate to the SPX and VIX markets is the inversion of convex ordering property: the fact that, even though associated local variances are smaller than instantaneous variances in convex order, the VIX squared is larger in convex order in the associated local volatility model than in the original model for short maturities. We argue and numerically demonstrate that, when the (typically negative) spot–vol correlation is large enough in absolute value, (a) traditional stochastic volatility models with large mean reversion, and (b) rough volatility models with small Hurst exponent, satisfy the inversion of convex ordering property, and more generally can reproduce the market term-structure of convex ordering of the local and stochastic squared VIX.

A new method for sequential learning of states and parameters for state-space models: the particle swarm learning optimization

ABSTRACT Accuracy of parameter estimation and efficiency of state simulation are common concerns in the implementation of state-space models. Even widely used methods such as Kalman filters with MCMC and Particle Filter, still present concerns with efficiency and accuracy, despite their successful results in their respective applications.This article presents a new method combining the structure of particle learning and bare bones particle swarm optimization (BBPSO) to the process of smoothing and filtering the states in the state-space models, thus overcoming the efficiency and accuracy problems. Sampling importance re-sampling is used to estimate the states of the model, then the parameters can be estimated via BBPSO, as an alternative to the kernel approximation of Liu and West. Our method is applied to stochastic volatility and AR(1) state-space models. Empirical results with Ibovespa and SP500 index show better performance when compared to particle filters, thus improving efficiency and accuracy.

Can International Market Indices Estimate TASI’s Movements? The ARIMA Model

This study investigates the effectiveness of six of the key international indices in estimating Saudi financial market (TADAWUL) index (TASI) movement. To investigate the relationship between TASI and other variables, six equations were built using two independent variables of time and international index, while TASI was the dependent variable. Linear, logarithmic, quadratic, cubic, power, and exponential equations were separately used to achieve the targeted results. The results reveal that power equation is the best equation for forecasting the TASI index with a low error rate and high determination coefficient. Additionally, findings of the AutoRegressive Integrated Moving Average (ARIMA) model represent the most important variables to use in order to build a prediction model that can estimate the TASI index. The ARIMA model (with Expert Modeler) coefficients are described as ARIMA (0,1,14). The results show that the SP500, NIKKEI, CAC40, and HSI indices are the most suitable variables for estimating TASI with an R2 and RMSE equal to 0.993 and 113, respectively. This relationship can be used on the previous day to estimate the opening price of TASI based on the closing prices of international indices.