Index Returns Research Articles

Market timing with moving average distance: International evidence

We explore the ability of the distance between short- and long-run moving averages, called MAD, to predict future returns of international market-wide indices. MAD portfolios yield abnormal profits after transaction costs, which do not reverse in the long run. This suggests that anchoring to long-run moving averages is a global phenomenon that applies also to market-wide indices. The annualized MAD portfolios’ alpha values are double-digit, with Sharpe ratios significantly higher than the global benchmarks. Similar results for developed economies and developed markets indicate that international diversification is still effective and offers significant economic benefits even among developed countries.

Unified specification tests in partially linear time series models

Based on a residual marked empirical process, Cramér–von Mises and Kolmogorov–Smirnov tests are proposed for the correct specification of the nonparametric components in partially linear time series models. The tests are unified in the sense that the asymptotic distribution of residual marked empirical process is invariant across different nν-consistent estimators in calculating residuals (where ν>1/4) under the null. In addition, the residual marked empirical process has the same power property under the sequence of local alternatives regardless of the estimators used. Achieved through a projection method, these features also enable using a computationally convenient multiplier bootstrap to approximate the unified null distributions of the test statistics. Simulations show satisfactory finite-sample performance of the proposed method. The application to validate the parametric form of conditional variance in the ARCH-X model is also highlighted, along with an empirical analysis of the conditional variance of the FTSE 100 index return series.

Sentiment and Stock Characteristics: Comprehensive Study of Individual Investor Influence on Returns, Volatility, and Trading Volumes

Abstract Background Traditional asset pricing models face challenges from financial anomalies, prompting exploration through behavioural finance theory. This study analyses the nuanced relationship between individual investor sentiment and key stock market variables. Objectives To assess the impact of individual investor sentiment on stock returns, volatilities, and trading volumes using the American Association of Individual Investors (AAII) sentiment index. Methods/Approach Using regression models, we examine the relationship between individual investor sentiment and various stock characteristics across 480 components of the Standard & Poor's 500 index. Results We find a positive relationship between the AAII sentiment index and stock returns and a negative relationship with volatility and trading volume. Conclusions Our study contributes to understanding the intricate role of individual investor sentiment in financial markets.

Baseline Neck Disability Index and Patient-Reported Outcomes Measurement Information System Physical Function Predict Postoperative Return to Normal in Cervical Spine Surgery.

Recent studies assessing the importance of various preoperative factors on postoperative outcomes following spine surgery have uncovered several important variables that influence subjective and objective outcomes following cervical spine surgery, but it is still unclear which patients are most likely to benefit from operative management. The objective of this study was to assess whether preoperative patient-reported outcome measures (PROMs) can be used to predict which patients achieve "normal" levels of pain and function after surgery. This was a prospective cohort study. This study included all adult patients undergoing cervical spine surgery by 1 of 7 senior spine surgeons at our institution between 2016 and 2018. Of the 164 patients who were eligible for 6-month follow-up at the time that study data were collected, 139 had available follow-up data and were included in our analysis. Patients completed the Neck Disability Index (NDI) as well as the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) and Pain Interference computer adaptive tests preoperatively and at 6 months postoperatively. Patients who achieved postoperative patient-acceptable symptom state (PASS) for NDI (≤17) and the normative mean (50) for PROMIS were identified. The relationship between preoperative PROMs and the probability of achieving PASS and the normative mean was assessed. One hundred thirty-nine patients met inclusion criteria with diagnoses of myelopathy (n = 36), radiculopathy (n = 48), and myeloradiculopathy (n = 49). For NDI, a 1-point worsening in the preoperative score resulted in an OR of achieving PASS of 0.96 (P < 0.001) in the overall population. This association held true for patients with radiculopathy (OR 0.96; P = 0.022) but not myelopathy (OR 0.98; P = 0.35). For PROMIS PF, a 1-point improvement in the preoperative score resulted in an OR of achieving the normative mean of 1.10 (P < 0.001). This association held true for patients with radiculopathy (OR 1.14; P = 0.033) but did not reach statistical significance for patients with myelopathy (OR 1.03; P = 0.515). Preoperative PROMs can predict postoperative benefit for patients undergoing cervical spine surgery, with worse baseline function associated with a lower likelihood of attaining PASS for NDI and the normative mean for PROMIS PF, especially for patients with radiculopathy. Baseline symptoms and function, including myelopathy or radiculopathy-dominant symptoms and preoperative PROMs, may predict postoperative outcomes.

Deformation of volatility curves of the Russian stock market on the example of margined options on futures contracts on the RTS index

We reassess the Russian stock market behavior in terms of the relationship between risk parameters and return on assets in connection with global structural changes in 2022. The subject of this study is volatility curves calculated from quotes of exchange options on RTS index futures. The purpose of the study is to analyze the relationship between volatility curves and the distribution of returns in 2015–2022, to analyze changes in the structure of this relationship in 2022, to assess the applicability of the GARCH model and to develop new investment and hedge strategies on the Russian stock market. The issue of interpreting the dynamics of volatility curves is disputable however, it was theoretically developed in the theory of valuation of derivative financial instruments. As a result of the study, unrelated structures of the distribution of returns and volatility curves for the RTS composite index were found throughout the entire period concerned, regardless of the presence of external shocks. It is concluded that in the Russian stock market, investors prefer protective options rather than speculative ones, despite the negative asymmetry of the distribution of RTS index returns. And despite the greater weight of positive returns in their distribution in each period under consideration, due to the technical features of the functioning of return indicators, the negative asymmetry of the return distribution does not necessarily correlate with growing volatility curves.

A modified grey wolf optimization-based dendritic neural model for stock index return prediction

The prediction of stock index returns has always been a hot topic, which is an important basis for asset allocation and investment decisions of business managers and most investors. In order to improve the accuracy of the prediction, this paper proposes the use of a model based on the combination of the Improved Grey Wolf Optimization (IGWO) and Dendritic Neural Model (DNM). DNM has a more transparent structure, as well as a complex nonlinear processing capability and a unique architecture of automatic pruning, which can deal with the complexity of financial markets. For the traditional grey wolf optimization algorithm, this study designs four strategies to improve it. (i) Develop a nonlinear control parameter for dynamic equilibrium in global and local search algorithms. (ii) Chaos theory is introduced to plan the weights of wolves (iii) A local search scheme applicable to the head wolf is developed. (iv) The method of Levy leap is utilized to help the algorithm get rid of the local optimum. A total of 10 other algorithms are compared and validated based on data from 9 world representative stock index markets. It is pointed out that using IGWO in conjunction with DNM can achieve higher accuracy prediction.

Identification and prediction of the degree of multidimensional returning to poverty risk for the household in China through the novel hybrid model: Based on the survey data of China Family Panel Studies (CFPS)

In China, absolute poverty has been effectively eliminated, but this does not signify the complete eradication of poverty. Instead, poverty persists in the forms of relative and secondary poverty. More concerningly, regions or households lifted out of poverty continue to face numerous risks of returning to poverty. In this context, measuring poverty solely based on monetary metrics is no longer adequate. Furthermore, the primary focus of grassroots governance has shifted from merely assessing poverty to accurately predicting the multifaceted risks associated with falling back into poverty. A multidimensional poverty indicator system is constructed to measure and predict the risk of multidimensional returning to poverty in China. Then, the Alkire–Forster counting method is applied to measure the risk of multidimensional poverty return and demonstrate the contribution of various indicators to multidimensional poverty return using tracking survey data from the China Family Panel Studies (CFPS). The results show that the multidimensional poverty return in China is mainly caused by the factors in two or three dimensions, where the social development capability dimension has the highest contribution to the multidimensional poverty return index with 43.12%, followed by the health and education dimensions. Moreover, according to the finding that the identification of multidimensional poverty varies with the values of the poverty cut-off, a novel and practicable method is proposed to classify the risk into three levels, noted as the high, medium and low levels. Consequently, a hybrid model is constructed to predict the risk of multidimensional poverty return by integrating the Archimedes optimization algorithm (AOA), variational mode decomposition (VMD) and Bi-directional long short-term memory (BiLSTM) neural networks. Finally, the performance of the constructed model is validated with an accuracy up to 99.81%. The constructed model's efforts outperform the traditional BiLSTM and several prevalent machine learning algorithms through extensive comparative experiments. These results illustrate that the proposed model can accurately and stably predict the potential risk of poverty return for multidimensional poverty groups and regions. In conclusion, drawing from the analysis of factors contributing to the risk of relapsing into poverty in this study, policy suggestions have been formulated focusing on education, social capacity enhancement, and healthcare system advancement. In summary, this paper provides new insights into the factors contributing to multidimensional poverty recurrence and the methods for assessing its risk levels, while also introducing a more precise approach to predicting these levels.

The Impact of the Last Two Presidential Periods on the Performance of the Indonesia Stock Exchanges: An Approach of Arbitrage Pricing Theory

This study aims to examine the influence of economic and non-economic macro factors on the performance of the Indonesia Stock Exchange (IDX) and the differences in their effects on the last two presidential terms using the Arbitrage Pricing Theory (APT) approach. The macro factors studied are an aggregate of 41 indicators of stock market index returns, benchmark interest rates, exchange rates, economic stability indicators, commodity prices, and corruption perception index (CPI). For the process of aggregation and identification of macro factors, this study uses Principal Component Analysis (PCA). The population studied were publicly listed companies on the IDX since October 2004 (299 companies) with a sample size of 157 companies. The observation period is 205 months: 120 months of the presidency of Susilo Bambang Yudhoyono (SBY) and 85 months of the presidency of Joko Widodo (JKW). The research design used the explanatory study method. The research model was analyzed using panel data regression. The results identified seven macro factor aggregates from 41 indicators studied: global index, macro economy, world oil prices, China index, Arabia index, inflation, and competitive resources. There was a simultaneous effect of macro factors on stock performance during the presidency of SBY and JKW. Partially, stock returns during the SBY era were positively influenced by the global index, world oil prices, the China index, the Arabian index, and inflation and negatively influenced by the macroeconomy. Meanwhile, during the JKW period, returns were positively influenced by the global, China, and Arabia indexes and inflation.

PERFORMANCE EVALUATION OF SIP AND LUMP-SUM INVESTMENT: A STUDY OF SMALL CAP MUTUAL FUNDS

In this paper, the Performance of 13 open-ended equity small cap schemes related to thirteen private sector mutual funds are evaluated i.e. ABSL Mutual fund, Axis Mutual Fund, DSP Mutual fund, Franklin templeton mutual fund, HDFC Mutual Fund, ICICI Prudential Mutual Fund, Kotak Mahindra Mutual Fund, HSBC Mutual Fund, Nippon India Mutual Fund, Quant Mutual Fund, SBI Mutual Fund, Sundaram Mutual Fund and Union Mutual Fund. The study period covers the period from 1stApril 2016 to 31st March 2023. For evaluating the performance of the selected mutual fund schemes, month-wise returns are compared with Benchmark- S&amp;P BSE Small Cap Index return. The rate which has no risk i.e. Risk Free Rate has been taken as average fixed deposit rate of SBI (State bank of India). Further, various different types of statistical tools like average, standard deviation, IRR, beta and the risk adjusted performance measures suggested by Sharpe (1966), Jenson (1968), and Treynor (1965) are employed to evaluate the performance for the above selected period. In this paper SIP and lump-sum investment returns are also compared of the above selected small cap schemes.

Analisis Kelayakan Investasi Smart Homestay Bu Ai Di Situs Wisata Gunung Padang

The research aims to assess the investment feasibility of Bu Ai's smart homestay. Bu Ai's smart homestay investment feasibility assessment is one of the Matching Fund Grant programs. The subject of this research is Bu Ai's Smart Homestay Business located in Kp.Gunung Padang, Karyamukti Kec.Campaka Kab.Cianjur West Java. This research was conducted for 5 months starting from August-December. The data used in this study are primary data. The methods used in this research are Payback Period, Net Present Value, Profitability Index and Internal Rate of Return. The results found that the feasibility assessment using PP, NPV, PI, &amp; IRR is not feasible. However, at this time the investment is still accepted due to other factors including the location of the homestay which is located in the Gunung Padang site tourism area which has the potential to continue to increase.

Effect of Market-Wide Investor Sentiment on South African Government Bond Indices of Varying Maturities under Changing Market Conditions

The excess levels of investor participation coupled with irrational behaviour in the South African bond market causes excess volatility, which in turn exposes investors to losses. Consequently, the study aims to examine the effect of market-wide investor sentiment on government bond index returns of varying maturities under changing market conditions. This study constructs a new market-wide investor sentiment index for South Africa and uses the two-state Markov regime-switching model for the sample period 2007/03 to 2024/01. The findings illustrate that the effect investor sentiment has on government bond indices returns of varying maturities is regime-specific and time-varying. For instance, the 1–3-year government index return and the over-12-year government bond index were negatively affected by investor sentiment in a bull market condition and not in a bear market condition. Moreover, the bullish market condition prevailed among the returns of selected government bond indices of varying maturities. The findings suggest that the government bond market is adaptive, as proposed by AMH, and contains alternating efficiencies. The study contributes to the emerging market literature, which is limited. That being said, it uses market-wide investor sentiment as a tool to make pronunciations on asset selection, portfolio formulation, and portfolio diversification, which assists in limiting investor losses. Moreover, the findings of the study contribute to settling the debate surrounding the efficiency of bond markets and the effect between market-wide sentiment and bond index returns in South Africa. That being said, it is nonlinear, which is a better modelled using nonlinear models and alternates with market conditions, making the government bond market adaptive.

Multi-objective optimal sizing and techno-economic analysis of on- and off-grid hybrid renewable energy systems for EV charging stations

Integrating electric vehicle charging stations (EVCSs) with renewable energy systems requires the consideration of several factors during the planning stage, including environmental impact, economic viability, grid reliability, and self-sufficiency. Therefore, this study conducts a multi-objective optimal sizing of on- and off-grid hybrid renewable energy systems for EVCSs. The sizing problem is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II). Subsequently, the best suitable solutions from the obtained non-dominated solutions are selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, prioritizing the objective functions based on diverse interests of different stakeholders (large and small private investors and governmental entities). Finally, a techno-economic analysis is made considering payback period, profitability index (PI), and internal rate of return (IRR). The results show that on-grid systems show high economic viability with payback periods between 1.98 and 7.72 years, an average PI of 5.07 and an average IRR of 23.97%. Although off-grid systems present lower economic viability with payback periods between 8.77 and 22.42 years, an average PI of 1.68 and an average IRR of 4.91%, in certain cases they reach investable levels with payback periods below 10 years, PI above 2, and IRR above the interest rate.

Research on the volatility forecasting model of KOSPI index returns using AR(M)-GARCH(P,Q) model

In this study, we estimated the volatility of the KOSPI index returns and analyzed volatility trends. The data used in the study consisted of monthly observations from January 2005 to December 2022, and the KOSPI index raw data was transformed into log returns. The volatility estimation model used the AR(m)-GARCH(p,q) model, which combines the autoregressive error model and the GARCH model that explains the persistence of volatility at low orders. The goodness of fit of the model was confirmed using the Portmanteau Q-test and LM-test. Applying the autoregressive error model revealed significant autocorrelation in the log returns of the KOSPI index at lags 3 and 6. Residual analysis indicated that the residuals followed white noise, but the squared residuals exhibited heteroscedasticity. Therefore, after fitting the autoregressive error model, we applied the GARCH model and conducted residual analysis, finding both the residuals and squared residuals significant at a 5% significance level. The volatility forecasting results indicated a continuous increase in volatility. The findings of this study are expected to provide important implications for policymakers responsible for risk management in the Korean stock market.

Conditional volatility targeting strategy considering jump effects: Evidence from sustainable ESG equity index

This paper considers the sustainable ESG equity index in the proposed conditional volatility targeting strategy. The research first detects jump risk using a jump test and then extends Bongaerts et al. (2020) by addressing jump risk and employing different volatility models to project volatilities under the conditional volatility targeting strategy. To capture consideration of the fact that index return dynamics, we propose an ARMA-GARCH jump model that can capture the characteristics of jump persistence, autocorrelation, and volatility clustering according to the return of the sustainable equity index. Our numerical analyses reveal that the portfolio allocation using a sustainable equity index to predict volatility, combined with a conditional volatility target strategy, can achieve higher performance. Furthermore, the proposed ARMA-GARCH jump model can enhance the performance with conditional volatility targeting strategy.

The Relationship between the Return of Energy Companies Listed on the Kazakhstan Stock Exchange and the Exchange Rate, KASE Index, and Gold Return: ARDL Bounds Value Approach

In a free market economy, stock market indices are influenced not only by national economic developments, but also by economic indicators such as gold, exchange rates, and oil. It is important to consider these indicators when analyzing the returns of companies traded on the stock exchange. Internal factors that impact stock market returns include the company's estimated earnings and changes in the company's financial structure. External factors include macroeconomic variables such as exchange rates, interest rates, gold prices, and Gross Domestic Product (GDP). A study analyzed the relationship between the returns of energy companies traded on KASE and the KASE index, exchange rate, and gold return during the period of 01.01.2023-01.04.2024 (328 trading days) using the ARDL Bounds Value Approach. The research findings indicate that the stock market composite index, foreign exchange, and gold returns have a long-term effect on the returns of energy companies. Particularly, the long-term effect of the stock market composite index return is found to be significant and positive for all three assets. Additionally, the effect of companies' past values has been observed as negative. According to the error correction model analysis, a key finding is that shocks to company returns will reach equilibrium in a short time, approximately one trading day. These results can provide decision support, especially for investors, when investing in energy companies.

ANALYZING FINANCIAL RATIOS' IMPACT ON STOCKS RETURNS: CASE STUDY OF LQ45 INDEX 2014-2023

This study examines the correlation between fundamental financial metrics and stock returns of Indonesia's LQ45 Index over a 10-year period (2014-2023). The study aims to provide actionable insights for investors and companies by identifying the most critical financial metrics in predicting stock price movements. By analyzing data from 16 consistently listed Indonesian companies, the study focuses on stocks that have consistently maintained their position in the index over the past decade, a gap that has been underexplored in prior research. The study employs various statistical tests to validate the regression model, with hypothesis testing conducted through t-test and F-tests to determine the significance of financial ratios on stock returns. The analysis shows that Return on Equity, Price to Earnings Ratio (PER), Price to Book Value (PBV), Free Cash Flow per Share to Price (PFCF), and Dividend Payout Rate (DPR) have a significant positive effect on stock return. The research recommends that retail investors prioritize the PFCF ratio when evaluating potential stock investments. Transparency in financial reporting is essential, with companies encouraged to provide accurate and comprehensive financial statements, including clear reporting of key financial metrics, to build investor confidence.

Market perceptions of distress and its impact on returns: evidence from the U.S. commercial real estate market

ABSTRACT This paper examines the relationship between market participants’ sentiment regarding distress in the real estate market, captured through internet search queries, and the U.S. commercial real estate market. We use Google search frequency to construct a measure of market perception of distress, the Market Distress Perception Index (MDP Index), and determine the extent to which this index can help predict future private commercial real estate returns in the U.S. The results show that our pessimistic sentiment index predicts returns of commercial real estate up to three quarters in advance. These results hold at the national, regional, and state levels and are robust across various model specifications. The predictability of the sentiment index is especially pronounced during recessionary periods. We also analyse different property sectors and find that the inverse relationship between the MDP Index and future private commercial real estate returns persists across all sectors except the industrial sector. The relationship is strongest for the office sector.

Index futures mispricing: a multi-regime approach to the NIFTY 50 Index futures

PurposeThis study investigates the mispricing dynamics of NIFTY 50 Index futures, drawing upon daily data spanning from January 2008 to July 2023.Design/methodology/approachThe study employs both a single regime analysis and a tri-regime model to understand the fluctuations in NIFTY 50 Index futures mispricing.FindingsThe study reveals a complex interplay between various market factors and mispricing, including forward-looking volatility (measured by the NIFVIX index), changes in open interest, underlying index return, futures volume, index volume and time to maturity. Additionally, the relationships are regime-dependent, specifically identifying the regime-dependent nature of the relationship between forward-looking volatility and mispricing, the impact of futures volume on mispricing, the effect of open interest on mispricing, the varying influence of index volume and the influence of time to maturity across the three distinct regimes.Practical implicationsThese findings offer valuable insights for policymakers and investors by providing a detailed understanding of futures market efficiency and potential arbitrage opportunities. The study emphasizes the importance of understanding market dynamics, transaction costs and timing, offering guidance to enhance market efficiency and capitalize on trading opportunities in the evolving Indian derivatives market.Originality/valueThe Vector Autoregression (VAR) and Threshold Vector Autoregression Regression (TVAR) models are deployed to disentangle the interrelationships between NIFTY 50 Index futures mispricing and related endogenous determinants.Research highlights (1)This study investigates the Nifty 50 Index futures mispricing across three distinct market regimes.(2)We highlight how factors like volatility, futures volume, and open interest vary in their impact.(3)The study employs vector auto-regressive and threshold vector auto-regressive models to explore the complex relationships influencing mispricing.(4)We provide valuable insights for investors and policymakers on improving market efficiency and identifying potential arbitrage opportunities.

Consumer confidence and cryptocurrency excess returns: A three-factor model

This study examined the relation between consumer confidence and cryptocurrency excess returns using a three-factor model of market, size and momentum. We analysed a dataset comprising 3318 cryptocurrencies from 1 January 2014 to 31 December 2022 based on the CoinMarketCap website. Results indicate a significant negative relation between the United States Consumer Confidence Index and cryptocurrency excess returns. The findings were reinforced based on robustness tests. This study contributes to consumer behaviour research and financial management within the cryptocurrency market. It also provides valuable insights for investors to strengthen their investment portfolios and for relevant authorities seeking to formulate effective policies for monitoring the cryptocurrency market.

Persistent and transient variance components in option pricing models with variance-dependent Kernel

This paper examines theoretically and empirically a variance-dependent pricing kernel in the continuous-time two-factor stochastic volatility (SV) model. We investigate the relevance of such a kernel in the joint modeling of index returns and option prices. We contrast the pricing performance of this model in capturing the term structure effects and smile/smirk patterns to discrete-time GARCH models with similar variance-dependent kernels. We find negative and significant risk premium for both volatility factors, implying that investors are willing to pay for insurance against increases in volatility risk, even if it has little persistence. In-sample, the component GARCH model exhibits a slightly better fit overall and across all maturity buckets than the two-factor SV model. However, the two-factor SV model reduces strike price bias, giving rise to the model’s ability in reconciling the physical and risk-neutral distribution. Out-of-sample, the two-factor SV model has better fit to data.