Longer Horizons Research Articles

Internal governance and investment efficiency: The role of non-CEO executives

In this paper, we reveal an overlooked but important role of corporate governance on investment efficiency: non-CEO executives. Internal governance, measured as the fraction of independent executives appointed before the current CEO, leads to a better investment efficiency. The governance effect is pronounced when executives have stronger incentives, such as a longer horizon or a higher shareholding ratio. To explain the promotion of investment efficiency, we find that independent executives help constrain CEO power. They also contribute to better quality of accounting information. Moreover, internal governance by non-CEO executives is hardly affected by external supervisors, and other internal governance mechanisms fail to enhance firms' investment efficiency, indicating the unique monitoring role of independent non-CEO executives. Our study demonstrates the significance of a democratic management team and the necessity to limit the power of CEOs to appoint new executives.

Predicting the volatility of major energy commodity prices: The dynamic persistence model

Time variation and persistence are crucial properties of volatility that are often studied separately in energy volatility forecasting models. Here, we propose a novel approach that allows shocks with heterogeneous persistence to vary smoothly over time, and thus model the two together. We argue that this is important because such dynamics arise naturally from the dynamic nature of shocks in energy commodities. We identify such dynamics from the data using localised regressions and build a model that significantly improves volatility forecasts. Such forecasting models, based on a rich persistence structure that varies smoothly over time, outperform state-of-the-art benchmark models and are particularly useful for forecasting over longer horizons.

Testing Forecasting Properties of Different Approaches to Interval Forecasting (Using the Example of Inflation in Russia)

The paper contains the main results of the study of the quality of interval forecasts based on quantile regression and smooth quantile regression models relative to interval forecasts based on OLS models for Russian inflation. All models in the work were built from the logic of the possibility of forecasting in real-time, in connection with which the authors used inflation that was not cleared of seasonality in the expression month to the same month of the previous year. For each forecast horizon, the authors developed a separate model from lagged values of inflation and regressors so that the known values of lagged variables were always used on the right side of the equation.The authors tested a wide range of specifications – from very compact autoregressive models with one or two lags to wide ones with three additional regressors in addition to inflation lags. The best quality from the point of view of CRPS (Continuous Ranked Probability Score) metrics typical for such a task were the specifications that included the only additional variable – the USD/RUB exchange rate. As the confidence interval narrowed and at longer horizons, quantile and smooth quantile models became increasingly better relative to OLS models according to the CRPS metric.According to the authors, in the presence of inflation targeting, qualitative inflation forecasts can be used by the Bank of Russia in conducting monetary policy when forecasting future inflation dynamics and stress-testing the Russian economy. In general, this can help increase the economic agents’ confidence in the Bank of Russia within the concept of rational expectations

Higher Precision Is Not Always Better: Search Algorithm and Consumer Engagement

On decentralized e-commerce platforms, search algorithms play a critical role in matching buyers and sellers. A typical search algorithm routinely refines and improves its catalog of data to increase search precision, but the effects of a more precise search are little known. We evaluate such effects via a 2019 quasiexperiment on a world-leading e-commerce platform in which the search algorithm refined some product categories into finer subgroups to allocate consumer queries to more relevant product listings. Our data cover millions of consumers’ search and purchase behaviors over six months across multiple search sessions and product categories, enabling us to investigate trade-offs over time and across categories. We find that a more precise search algorithm improves consumers’ click-through and purchase rates drastically and instantaneously, but it comes at the cost of a significant decrease in consumer engagement and unplanned purchases over a longer time horizon. On average, consumers who used to spend more time searching now conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the week after the search precision increases. Our examination of the mechanisms behind these consequences calls for more careful search algorithm designs that account for not only instant conversion based on search precision but also consumer engagement and sellers’ strategic responses in the longer horizon. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the Key Program of the National Natural Science Foundation of China [Grants 72141305 and 72192803] and the Ministry of Education, People’s Republic of China (Fundamental Research Funds for the Central Universities), both to L. Fang. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00478 .

Presidential economic approval rating and global foreign exchange market volatility

This paper examines the influence of presidential economic approval rating (PEAR) on the volatility of 15 major foreign exchange (FX) markets. The study reveals that the PEAR index serves as an insightful predictor for forecasting certain FX market volatilities, demonstrating predictive accuracy both in- and out-of-sample period. This predictability endures over longer horizons and withstands various robustness assessments. Notably, the forecasting efficacy of PEAR surpasses that of uncertainty factors and macroeconomic indicators. Additionally, we illustrate that a combined forecast incorporating all predictors enhances forecasting robustness. Finally, our findings indicate that PEAR can also elucidate future jump risks and returns in FX markets.

Reprint of: How do households respond to income shocks?

We use panel data from the Italian Survey of Household Income and Wealth from 1991 to 2016 to document what components of the household budget constraint change in response to shocks to household labor income, both over shorter and over longer horizons. Consumption and wealth responses are informative about the household consumption (or savings) function and thus about what class of consumption-savings model best describes the data. Empirically, we first show that shocks to labor income are associated with negligible changes in transfers and non-labor income components, modest changes in consumption expenditures, and large changes in wealth. To understand the wealth response we then split households into a sample that does not own business or real estate wealth, and a sample that does. For the first group, we find that consumption responses are more substantial (and increasing with the horizon of the income shock) and wealth responses are much smaller (and mildly increasing with the income shock horizon). Turning to theory, we argue that for this group, a simple extension of the standard permanent income hypothesis (PIH) consumption function that allows for partial insurance against even permanent income shocks explains the consumption and wealth responses well, both at short and long horizons. For the second group with business wealth or real estate wealth the standard framework cannot explain the large changes in wealth associated with income shocks. We conclude that models which include shocks to the value of household wealth are necessary to fully evaluate the sources and consequences of household resource risk.

Interplay between oil prices, country risks, and stock returns in the context of global conflict: A PVAR approach

Employing the panel vector autoregressive (PVAR) modeling, we analyze the interplay among oil prices, country risks, and stock returns across twenty-nine economies from February 2005 to August 2020. We find that, in the short run, rising oil prices temporarily boost stock prices by reducing country risk. However, over longer horizons, reductions in country risk are linked to lower stock returns. Moreover, in the interaction between stock and oil markets we identify the heterogeneity of three forms of country risk: economic, financial, and political, particularly when comparing developed and developing economies. Findings, offering new insights into the linkages between oil and stock markets, especially in the context of increased global conflict context, are of much value for investment strategies and policy formulations aimed at mitigating risk.

GDP and temperature: Evidence on cross-country response heterogeneity

We estimate individual country real GDP per capita growth responses to country, global, and idiosyncratic temperature shocks. Negative growth responses to country and global temperature at longer horizons are found for all Group of Seven countries. Positive country (global) responses are found for approximately eight (seven) of the nine poorest countries at longer horizons. Both country and idiosyncratic temperature shocks have more negative than positive effects on growth across countries, but it is more evenly split for the global temperature shock. After controlling for average temperature, positive growth responses to global temperature shocks are more likely for countries that are poorer, have experienced slower growth, are more educated (higher high school attainment), and more open to trade.

Integration among the BRICS stock markets: Filtering out global factors

ABSTRACT Despite the prominence of the BRICS bloc, little attention has been given to their stock market co-movement. This study investigates the time- and frequency-varying integration of the BRICS stock markets including new BRICS + members with active markets. We explore whether market integration is driven by common exposure to global factors using the DCC-GARCH model and wavelet coherence. Results show that removing global influences reduces variation, range and, for some country pairs, magnitude in correlations. The markets of India and Russia are most integrated with other markets in the bloc, while the markets of China and Brazil are most segmented from others in the bloc. New BRICS members, Saudi Arabia, Egypt and the United Arab Emirates, exhibit strong integration with some original bloc members. Crisis impacts are more systemic rather than specific to market linkages, with stable co-movements after filtering returns. Wavelet analysis shows that most integration between markets occurs over longer horizons. These findings have implications for portfolio construction and policymakers tasked with financial stability.

Does Contrarian Investing Beat the Conventional Strategies and the Index?

Traditional momentum strategies on the stock market are implemented in accordance with the efficient market hypothesis and involve making investments in accordance with market trends. However, this hypothesis has been repeatedly criticized by supporters of behavioral finance, who allow the irrational nature of investment decisions, which led to the emergence of contrarian investment strategies, based on the overreaction hypothesis and the reversal effect (over a longer horizon, loser stocks outperform winners), according to which investments are made in opposite direction to the market, involves buying the losing stocks and selling the winners. According to contrarian investment strategies, when buying shares, investors prefer a lower level of P/E ratios (Price – earnings ratio), which indicates the valuation multiple placed to the company’s market price vis-à-vis its net profits. The article examines the hypothesis that P/E ratio and contrarian investment are related, based on data for 2010–2022 for 50 shares of India, whose stock market is developing rapidly, examines the differences between the top ten P/E stocks and the bottom ten P/E stocks using inflation adjusted return on investment (ROI) to determine, whether or not, an investment in such stocks would generate sufficient returns to outpace inflation over a period of ten years. The article tests 3 hypotheses that investing in stocks with low P/E ratios in the Nifty 50 index (a live benchmark that illustrates how each industry contributes to India’s GDP) will generate higher returns than investing in stocks with high P/E ratios, over a long-term horizon (H1), inflation (Н2), the Nifty 50 index (Н3). The sources of data were finalized to be BSE Official Website, NSE Official Website, Yahoo Finance, Screener, Annual Reports, etc. The article calculates the profits for top 10 P/E stocks (conventional strategy) and bottom 10 P/E stocks (contrarian investing), and determining the returns from top 10 P/E stocks and the bottom 10 P/E stocks (when calculating for 10 years, the same steps were repeated: synchronously, once a year, both types of shares were bought at Purchase Price and sold a year later at Market Price). The calculations confirmed all 3 proposed hypotheses. Contrarian investing delivered returns of around 3.64 times the initial investment, whereas conventional investing produced returns of about 77.5% when comparing the top 10 P/E stocks to the bottom 10 P/E stocks in the Nifty 50 index. An outstanding return of 84.11%, which is a strong return when adjusted for inflation, was achieved using the contrarian strategy. As a result of the conventional strategy’s loss of 10.45% when adjusted for inflation, the study also shows that contrarian investing can assist an investor in hedging against inflation. This emphasizes how crucial it is to take a contrarian attitude when making stock market investments. At the same time, contrarian investment returns of 3.64 times the initial investment over this period surpass the benchmark Nifty 50 Index returns, which is 3.24 times.

Predictions from Generative Artificial Intelligence Models: Towards a New Benchmark in Forecasting Practice

This paper aims to determine whether there is a case for promoting a new benchmark for forecasting practice via the innovative application of generative artificial intelligence (Gen-AI) for predicting the future. Today, forecasts can be generated via Gen-AI models without the need for an in-depth understanding of forecasting theory, practice, or coding. Therefore, using three datasets, we present a comparative analysis of forecasts from Gen-AI models against forecasts from seven univariate and automated models from the forecast package in R, covering both parametric and non-parametric forecasting techniques. In some cases, we find statistically significant evidence to conclude that forecasts from Gen-AI models can outperform forecasts from popular benchmarks like seasonal ARIMA, seasonal naïve, exponential smoothing, and Theta forecasts (to name a few). Our findings also indicate that the accuracy of forecasts from Gen-AI models can vary not only based on the underlying data structure but also on the quality of prompt engineering (thus highlighting the continued importance of forecasting education), with the forecast accuracy appearing to improve at longer horizons. Therefore, we find some evidence towards promoting forecasts from Gen-AI models as benchmarks in future forecasting practice. However, at present, users are cautioned against reliability issues and Gen-AI being a black box in some cases.

The Impact of Differential COVID-19 Pandemic Containment Policies on International Trade and Capital Flows

ABSTRACT This article develops a two-period two-country model to examine the impact of differential COVID-19 pandemic containment policies on international trade and capital flows. The theoretical analysis shows that asymmetric labor supply shocks resulting from the pandemic generate capital outflows from more affected countries to less affected ones, with the former running current account deficits. The greater the asymmetry in supply shocks, the more pronounced the impact on cross-border capital and trade flows. As the pandemic abated, these flows reversed, creating significant fluctuations over the pandemic cycle. However, globalization acted as a risk-sharing mechanism, moderating potential welfare losses. Using panel data from 42 countries, we investigate how specific containment policies like workplace closures and home isolation mandates affect trade and capital flows. Contrary to findings using higher frequency data, our empirical results confirm key predictions of our model, suggesting more stringent temporary containments, while dampening economic activity initially, may reduce COVID-19 cases and induce current account surpluses and capital inflows over a longer horizon.

Forecasting global stock market volatilities: A shrinkage heterogeneous autoregressive (HAR) model with a large cross-market predictor set

We propose a shrinkage heterogeneous autoregressive (HAR) model to explore the predictability of global stock market volatilities. To this end, we construct a big data environment using international stock market data, which comprises over 200 cross-market predictors, including realized variances (RVs), continuous components, jumps, leverages, overnight returns, and uncertainty indices. Two shrinkage models (LASSO and ENet) are considered for volatility modeling and forecasting. The results demonstrate that these models, notably LASSO, consistently improve predictive performance across global stock markets and daily, weekly, and monthly horizons. Their superiority extends to directional and density forecasting as well as asset allocation. There are several empirical findings of shrinkage: (i) the shrinkage degree varies significantly across forecast horizons, with longer horizons implying more predictors are needed for accurate estimation; (ii) no country/region's predictors show absolute superiority and utilize cross-market information leads to larger predictive gains, particularly for longer horizons; (iii) the predictors except for RVs and continuous components are more powerful, especially over longer horizons; and (iv) the uncertainty indices are the strongest predictors for all horizons.

A Horizon‐Based Decomposition of Mutual Fund Value Added Using Transactions

ABSTRACTWe decompose mutual fund value added by the length of funds' holdings using transaction‐level data. We motivate our decomposition with a model featuring horizon‐specific investment ideas, where short‐term ideas are less scalable because the associated trades cannot be spread over time. Fund turnover correlates negatively with the horizon over which value is added and positively with price impact costs. As predicted, holdings of high‐turnover funds add a substantial amount of value in the first two weeks, of which more than 80% is earned on Federal Open Market Committee (FOMC) and earnings announcement days. Holdings of low‐turnover funds add value only over longer horizons.

From dominant to producer currency pricing: Dynamics of Chilean exports

We revisit a central question for international macroeconomics: the response of export prices and quantities to movements in the exchange rate (ER). We use granular export data for Chile and study how the effects of ER movements vary over time with the currency of invoicing and the destination of exports. For prices, we find that the short-run effects of bilateral ER movements vanish when controlling for U.S. dollar ER, which supports dominant currency pricing. However, over longer horizons a more significant role is played by bilateral ER movements, in line with the predictions of producer currency pricing. These dynamics do not depend on the invoicing currency. The results we find for quantities support the view that bilateral exchange rate movements contribute to macroeconomic adjustment through export volumes over the medium term.

That I can wait: The effect of promotion framing on consumer preferences

Sales managers often struggle to create effective combinations of promotion framing and promotion reward timing. However, whether gain-framing (vs. loss-reduction framing) promotions are better suited to be combined with immediate or delayed promotions remains an open question. In three studies, we show that the gain (vs. loss-reduction) framing of promotion information activates a high (vs. low) construal level, which leads to a higher preference for delayed (vs. immediate) promotions. We also find that time perception moderates the proposed effect. Specifically, consumers with time-overestimating (vs. time-underestimating) perception are more likely to choose immediate (delayed) promotions when they are at a low (high) construal level. Our findings provide insights for marketers and retailers that consumers would like to wait for a higher reward in a longer horizon under the gain promotion frame, while they prefer an immediate reward even if the benefit is much lower under the loss-reduction promotion frame.

Market Equilibrium and the Cost of Capital with Heterogeneous Investment Horizons

Expected returns, variances, betas, and alphas are all non-linear functions of the investment horizon. This seems to be a fatal conceptual problem for the capital asset pricing model (CAPM), which assumes a unique common horizon for all investors. We show that under the standard assumptions, the theoretical CAPM equilibrium surprisingly holds with the 1-period parameters, even when investors have heterogeneous and possibly much longer horizons. This is true not only for risk-averse investors, but for any investors with non-decreasing preferences, including prospect theory investors. Thus, the widespread practice of using monthly betas to estimate the cost of capital is theoretically justified.

Performance dispersion among target date funds

There are significant differences in the performance of Target Date Funds (TDFs) with the same target year. Using a unique dataset from Morningstar, we show that within the same target year, funds with lower than the average expense ratio, or higher than average allocation to equities, outperform similar funds. This outperformance exists across all target year groups and is economically meaningful. Furthermore, deviations in the equity allocation have a greater impact on performance than does expense ratio. Using bootstrap simulations to investigate the impact over a longer horizon, we show that deviations from the average allocations or expense ratios have a meaningful impact on the retirement savings of an average investor.

Automated Model Selection Using Bayesian Optimization and the Asynchronous Successive Halving Algorithm for Predicting Daily Minimum and Maximum Temperatures

This study addresses the crucial role of temperature forecasting, particularly in agricultural contexts, where daily maximum (Tmax) and minimum (Tmin) temperatures significantly impact crop growth and irrigation planning. While machine learning (ML) models offer a promising avenue for temperature forecasts, the challenge lies in efficiently training multiple models and optimizing their parameters. This research addresses a research gap by proposing advanced ML algorithms for multi-step-ahead Tmax and Tmin forecasting across various weather stations in Bangladesh. The study employs Bayesian optimization and the asynchronous successive halving algorithm (ASHA) to automatically select top-performing ML models by tuning hyperparameters. While both the Bayesian and ASHA optimizations yield satisfactory results, ASHA requires less computational time for convergence. Notably, different top-performing models emerge for Tmax and Tmin across various forecast horizons. The evaluation metrics on the test dataset confirm higher accuracy, efficiency coefficients, and agreement indices, along with lower error values for both Tmax and Tmin forecasts at different weather stations. Notably, the forecasting accuracy decreases with longer horizons, emphasizing the superiority of one-step-ahead predictions. The automated model selection approach using Bayesian and ASHA optimization algorithms proves promising for enhancing the precision of multi-step-ahead temperature forecasting, with potential applications in diverse geographical locations.

Will COVID “encourage” B2B and data exchange engineering in logistic firms?

Abstract The goal of this article is to develop recommendations to assist logistic companies in formulating their B2B deployment policies as part of their engineering infrastructure strategy. It is postulated that logistic firms are likely to integrate B2B into their organizational structure in order to obtain a competitive advantage. The research examines the situation not only in the period immediately prior (2006) and after (2010) the 2008–2009 economic crisis, but also over a longer period of the following years in order to discover an eventual impact of the crisis on B2B deployment (2014, 2018). The results obtained from the 2008–2009 economic crisis are then applied analogically to the current 2020 coronavirus economic recession, where they serve as a foundation for formulating B2B deployment policy recommendations. We ascertained that B2B deployment increases in time of intensified market competition due to an economic crisis or recession and this tendency persists even in a longer horizon after the macroeconomic instability culmination. B2B systems can thus act as a significant “tool” for enhancing business competitiveness, which may be crucial for their economic “survival.”