Value Timing: Risk and Return Across Asset Classes

Introduction to The Problem: The movement of the Islamic stock index can be influenced by changes in domestic macroeconomic conditions, not only domestic macroeconomics but also influenced by the stock markets of other countries.Purpose/Objective Study: The main objective of this study is to analyze the influence of the domestic macroeconomic and global stock indices on the Indonesian sharia stock index.Design/Methodology/Approach: This study uses a quantitative methodology with secondary data. The data sample method is saturated sample that all members of the population are used to be the research sample. The type of research data is monthly time series with a time period from May 2011 - July 2019, the selection of the Vactor Error Correction Model (VECM) research method based on the stationarity of the data on the first difference and the existence of cointegration models.Findings: The results showed in the short term all variables did not show a significant effect. In the long run, interest rates have a negative effect, while the exchange rate shows a positive effect on the movement of Islamic stock price indexes. Global stock indices such as the Shanghai Stock Exchange Index show a negative effect, and the Standard & Poor's 500 index shows a positive effect. While the Nikkei 225 index did not show a significant effect on the Indonesian Islamic stock index.

Seasonality fluctuations in a time series affect the asset classes in a particular way in terms of returns and movement trends for different asset classes. A time series may vary from being minute-wise, hourly, daily, weekly, monthly, quarterly, and annually.This study has attempted to understand the coupled outcome of seasonality index, the standard deviation in monthly seasonality patterns, and the correlation of different asset classes to check the time series model’s applicability and determine whether the time series model is stationary not. It has analyzed trends in the asset classes when the series is deseasonalized if seasonality is present. Usually, seasonal and non-seasonal components are already enbibed as values follow random-walk, so the time series is majorly categorized as non-stationary. The paper discovers and forms any available relation between asset classes and seasonality at regular intervals. Analyzing seasonal patterns is vital. It provides valuable information to potential investors, and analyzing seasonality concerning the Indian market is vital. Trend analysis of various stock markets has been incorporated in many studies, but considering all asset classes for the Indian economy is rare. It helps readers understand how an asset’s monthly and yearly seasonality will affect asset classes’ movement. The paper uses four major asset categories for the study. The seasonality between asset classes is not that evident across all the asset classes. However, certain assets tend to show the correlation between periods and the price of a certain asset.

This study examines the long-term and short-term relationships among five major global stock indices—NSE NIFTY (India), S&P 500 (USA), FTSE 100 (UK), Hang Seng (Hong Kong), and Nikkei 225 (Japan)—over the period from 2008 to 2023. Using advanced econometric techniques such as Johansen cointegration tests, Engle-Granger residual cointegration test and the Vector Error Correction Model (VECM), the research explores the degree of interconnectedness and cointegration among these indices. The results indicate strong positive correlations between NSE NIFTY, S&P 500, and Nikkei 225, suggesting a growing synchronization of these markets due to global economic integration. Johansen's test reveals the presence of multiple long-term equilibrium relationships, affirming that despite short-term deviations, these markets tend to move together in response to global macroeconomic factors. The analysis further highlights that the S&P 500 and FTSE 100 indices exhibit significant error correction mechanisms, underscoring their leadership role in global market dynamics. In contrast, NSE NIFTY shows a weaker adjustment toward long-term equilibrium, indicating its reactive nature to changes in other global indices, particularly the S&P 500. The study finds that while Hang Seng and Nikkei 225 are connected globally, their short-term linkages appear weaker, likely influenced by regional factors. Overall, the results emphasize the critical influence of the U.S. and Japanese markets on NSE NIFTY, with both long-term cointegration and short-term market dynamics shaping the Indian stock market’s movements. This research offers key insights for investors seeking to understand global stock market interdependencies and manage portfolio risk.

Uniqueness in economies and stock markets has given rise to an interesting domain of exploring data mining techniques across global indices. Previously, very few studies have attempted to compare the performance of data mining techniques in diverse markets. The current study adds to the understanding regarding the variations in performance of data mining techniques across the global stock indices. We compared the performance of Neural Networks and Support Vector Machines using accuracy measures Mean Absolute Error (MAE) and R­­­­oot Mean Square Error (RMSE) across seven major stock markets. For prediction purpose, technical analysis has been employed on selected indicators based on daily values of indices spanning a period of 12 years. We created 196 data sets spanning different time periods for model building such as 1 year, 2 years, 3 years, 4 years, 6 years and 12 years for selected seven stock indices. Based on prediction models built using Neural Networks and Support Vector Machines, the findings of the study indicate there is a significant difference, both for MAE and RMSE, across the selected global indices. Also, Mean Absolute Error and Root Mean Square Error of models built using NN were greater than Mean Absolute Error and Root Mean Square Error of models built using SVM.

We uncover a positive stock market risk-return tradeoff after controlling for the covariance of market returns with the value premium. Fama and French (1996) conjecture that the value premium proxies for investment opportunities; therefore, by ignoring it, early specifications suffer from an omitted variable problem that causes a downward bias in the risk-return tradeoff estimation. We also document a positive relation between the value premium and its conditional variance, and the estimated conditional value premium is strongly countercyclical. The latter evidence supports the view that value is riskier than growth in bad times, when the price of risk is high.

Can Rational Investors Overreact?

Asset Allocation Via Clustering or, Just How Useful Is My Stylebox? And Are Most Hedge Funds Really the Same?

Objective - The objective of this study is to determine how the movement of several indices and indicators of the global economy affect the change in investment by foreign fund flows in the Indonesia Stock Exchange (BEI). Methodology/Technique - Some global stock indices used in this study comprise the Dow Jones index, the Nikkei 225 index, the Shanghai index (SSE) and the Singapore Index (STI). Data were taken monthly from March 2009 to June 2014. Findings - The results obtained from this study indicate that the Dow Jones index and the STI index have a significant positive effect on the movement of foreign investmentsin the Stock Exchange. In contrast, the movement of world oil prices and exchange rate of the IDR/USD have a significant negative effect on the movement of foreign investments in the BEI. Novelty - The results of this study reinforces that the depreciation of the rupiah against the USD is an indication that the fundamentals of the Indonesian economy is not strong enough. Type of Paper: Empirical Keywords: Dow Jones, Nikkei 225 Index, Shanghai Index (SSE), STI Index, World Oil Prices, World Gold Price, Exchange Rate IDR/USD

Network analysis of global stock markets at the beginning of the coronavirus disease (Covid-19) outbreak

This study explores the time–frequency return connectedness of the four most relevant asset classes namely, equity, digital assets, commodity, and fixed income. To do so, we use the novel proxies of the S&P500 Index for equity, the S&P Cryptocurrency MegaCAP Index for digital assets, the S&P Goldman Sachs Commodity Index for commodity, and the S&P Global Developed Sovereign Bond Index for fixed income, and also employ the wavelet analysis for daily data over the period 2017: M02 to 2021: M09. In contrast to the pre-COVID-19 period, our findings indicate that the interdependence between the selected asset classes has intensified across all time scales and frequency bands during the COVID-19 crisis, proving the lack of hedging opportunities. Besides, the findings reveal that there is a significant lead-lag relationship between time series at medium and low frequencies during the research period, and the directional connectedness among asset classes is sensitive to frequencies. Especially, the co-movements among the pairs are pronounced during the COVID-19 outbreak. Remarkably, the wavelet-based Granger causality test corroborates the wavelet results and underscores there is a significant causal link between the variables during COVID compared to pre-COVID. Moreover, the results of the portfolio risk analysis by employing the value at risk (VaR) measure indicate that portfolio diversity advantages vary among frequency and across time. The results of the present study provide insight and might help foreign portfolio investors diversify their portfolios across different asset classes.

Many index providers claim that the book-to-price ratio is no longer a sufficient descriptor of the value factor. They argue that it has become outdated because reported book value ignores investments into intangible assets. As a solution, they propose including other valuation ratios, such as earnings-to-price, sales-to-price, cash flow-to-price, or dividend yield. However, this solution overlooks a superior alternative: Intangible capital can be estimated and added to the book value. In this article, the authors compare these alternative solutions. Beyond a naive analysis of performance, they ask (1) whether improvements persist when considering implicit exposure to other factors and (2) whether they align with a risk-based explanation for the value factor. The authors show that including unrecorded intangibles in the book value increases the value premium and aligns with risk-based explanations. By contrast, other valuation ratios do not add investment value beyond picking up implicit exposure to factors other than value. Such valuation ratios also fail to improve the alignment of value strategies with risk-based explanations. TOPICS:Factor-based models, style investing, analysis of individual factors/risk premia Key Findings • Intangibles are largely ignored when using book-to-price as a value factor proxy. Two different remedies have been proposed: using alternative valuation ratios or adjusting the book value to include intangibles. • Alternative valuation ratios do not add investment value beyond picking up implicit exposures to other factors. The intangible adjustment, however, does add investment value for multifactor investors. • The intangible-adjusted book-to-price factor aligns well with a risk-based explanation for the value premium, whereas some alternative valuation ratios fail to pass this hurdle.

Following the collapse of Silicon Valley Bank and Signature Bank, First Republic Bank collapsed and is considered the second-largest bank failure in U.S. history. These bank runs can have a cascading or contagion effect on other large banks, and U.S. banking crises can flare up again. We examine the effect of the First Republic bank run on top U.S. banks, U.S. stock indices, and global stock indices using standard event study methodology. We report abnormal returns and cumulative abnormal returns for the event day (t = May 01, 2023) and the 10-day event window (t-5 to t+5), respectively, using data from the 120-day estimation window. The results indicate that on the event day, only JP Morgan Bank's returns were negative, while other banks acted as safe havens for investors. No significant change in returns on the event day is observed for U.S. sector indices (except for the healthcare sector) and global stock exchanges, except for the European and Chinese markets. During the event window, the occurrence of the event significantly affects bank returns after the event date, but no significant effect is found before the event date. Similarly, the healthcare and transportation sectors are more affected than other sectors, while the U.S. and Canadian stock markets seem to be more susceptible to the bank run. Overall, the results suggest that the U.S. government should take decisive initiatives to stop the ripple effect and protect the entire financial system.

This paper evaluates a hypothetical portfolio based on an equal-weight investment rule involving four asset classes: domestics, industrialized countries excluding the U.S., emerging market countries, and global bonds. Portfolio results using three alternative re-balancing rules are compared with a composite benchmark index. The simulation period runs from July 1996 through January 2010, which is dictated by the availability of data on the underlying asset classes. The equal-weight investment strategy is superior to investing the composite benchmark index in all three rules examined. The first involves no rebalancing over the simulation period. This provides the best performance versus the composite benchmark. The second rule collects and rebalances dividends according to the equal-weight investment rule. The third collects both dividends and capital gains and reallocates them among the four asset classes using the same equal-weight investment rule. Of these rules, there is a slight surrendering of return as one moves to more complex rebalancing rules. Additionally, the variability of portfolio return increases marginally as one moves from the baseline rule through the two rebalancing rules. Thus, a good case could be made to following a fixed investment rule and allowing the market to rebalance the portfolio.

Intermediary Leverage and Currency Risk Premium

Intermediary Funding Cost and Short-Term Risk Premia