Market Timing Research Articles

Probability weighting and equity premium prediction: Investing with optimism

AbstractEmpirically motivated theoretical models of probability weighting which overweight tail events are finding many applications in finance. However, probability weighting has not yet been applied to equity premium prediction or to constructing optimal market timing investment strategies. We show that a measure of market optimism from a representative agent asset pricing model with probability weighting can be used to construct optimal dynamic investment strategies that outperform the buy‐and‐hold strategy and strategies generated by 17 leading equity premium predictors. We further show that this theory‐based measure of market optimism predicts the equity premium and market Sharpe ratio in‐sample and out‐of‐sample. The predictability is not subsumed by disaster probabilities, market sentiment, or market skewness. Our results indicate that our theory‐based measure provides a distinct channel for predicting aggregate stock returns.

Does managerial market timing with stock repurchases exist in stock market?: Evidence from Thailand

This study examines the presence of managerial market timing with stock repurchases in the Stock Exchange of Thailand (SET) from 2008 to 2017. We find that managerial market timing with stock repurchases does not exist in an emerging market (namely, Thailand) supporting the theory of pseudo market timing, as the probability of stock repurchases is not related to undervalued stocks. However, there are other motivations for conducting stock repurchases rather than managerial market timing including the distribution of excessive cash flow due to high operating income and no good future investment opportunity, and the change of capital structure because of underleverage. Our findings are supportive to the free cash flow, capital market allocation, and trade-off theories. In addition, we also find that timing firms tend to suffer from the deterioration of Tobin q ratio, market value added (MVA) and economic value added (EVA), and they do not rebalance their capital structure after this event.

Trends and Influencing Factors in Film Box Office: A Study on Chinese Domestic and International Films

At present, box office performance has become one of the critical indicators of a film's success. Both domestic and international film markets have shown steady growth; however, the box office performance of different films varies significantly. In light of this, this paper focuses on the box office development of domestic and foreign films, reviewing and analyzing the factors that influence the box office performance of many films. Additionally, a comparative analysis between domestic and foreign films is conducted. According to research findings, the primary factors affecting the box office performance of domestic films include the "mainstream" theme, storyline, realistic social topics, release timing, and multimedia promotion. In contrast, the factors influencing the box office performance of foreign films mainly involve the production company, country of origin, type of film, technological innovation, and online reputation. Based on these findings, this paper further analyzes the shortcomings that have led to the box office failures of both domestic and foreign films. Furthermore, recommendations for enhancing box office performance, applicable to all films, are proposed from four dimensions: film content, filming technology, market timing, and marketing strategies.

Impact of in-silico technologies in the development of high-risk medical devices on healthcare

Abstract This study examines the impact of in-silico technologies (IST), such as virtual cohorts and digital twins, on the development of high-risk medical devices within the healthcare sector. An iterative empirical approach combining a literature review, exploratory and in-depth interviews with stakeholders from academia, industry, regulators, healthcare professionals, and focus groups with patients across Europe, Japan, and the USA was employed to gain insight into the implications of IST on healthcare. The findings indicate that IST can reduce the time to market for medical devices, thereby facilitating faster, broader, and more equitable access for patients. The implementation of extensive early testing, optimized study designs and increased sample sizes in trials can facilitate the achievement of enhanced safety. The early identification of adverse effects can enhance the safety of medical devices and improve patient outcomes. Furthermore, IST may facilitate the inclusion of underrepresented groups, such as children, in clinical trials, thereby enhancing their representation in medical research. The reduction in costs associated with IST may facilitate innovation cycles. However, the impact on prices is contingent on competition. Furthermore, IST have the potential to reduce the need for animal testing. Nevertheless, several uncertainties and potential adverse effects have been identified. Physician training on new devices remains a bottleneck, which may limit the benefits of new products. Despite the advancements, a complete replacement of traditional clinical and preclinical trials with IST is not seen to be feasible. From a policy perspective, it is crucial to promote regulatory clarity by providing guidance for the usage of IST, to offer incentives for the adoption of IST, and to foster competition. These findings demonstrate the multifaceted potential benefits and challenges of IST, including enhanced safety, accessibility, and innovation in healthcare. Key messages • In-silico technologies can reduce time-to market of medical devices up medical device and improve safety for patients. • Challenges like physician training and regulatory guidance must be addressed to fully realize benefits of in silico technologies.

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.

Initial Public Offering (IPO) and Value Aggregation: a Systematic Literature Review

Objective: The objective of this study is to conduct a systematic literature review on Initial Public Offerings (IPO) and value aggregation, identifying the key articles and authors who investigate the topic. Theoretical Framework: The theoretical framework is based on life cycle, market timing, and pecking order theories, highlighting the benefits and risks of IPOs for companies, such as raising capital and increasing market value. Method: The method used was a systematic review, based on the Web of Science, focusing on articles about IPOs and value creation. Stages such as research operationalization, application of filters, and bibliometric analysis were followed. Results and Discussions: The results indicate a significant increase in the number of publications after 2007, with emphasis on the seminal work of Loughran and Ritter. The discussions reveal that most research focuses on pricing and governance aspects, with few investigations on financial value aggregation. Research Implications: The research implications suggest the need for future studies focusing on the financial impact of IPOs, especially in emerging markets. Originality/Value: The originality and value of this work lie in the systematization of a vast literature on IPOs and the identification of gaps for future research

The prescriptive nature of market timing and predictive portfolios

Abstract We explore the ability of market timing, model-based forecasts that include incremental information over and above momentum, to create long-short portfolios that consistently provide superior returns to traditional benchmarks, and frame our analysis in the idea of “prediction-led prescription” for forward-looking decision making. Our methodology consists of linear and non-linear forecasts with model selection, that predict the direction of future returns and apply then into the construction of portfolios. Our results strongly recommend that predictive-based portfolio allocation can produce excess returns and higher Sharpe ratios, above and beyond the individual return series that are utilized to create the forecasts and traditional portfolio benchmarks. The use of “predictive methods in portfolio construction reinforces the idea of prediction-led prescription” as portfolios are prescriptions for investment management and have an exclusively forward-looking nature.

Forecasting the equity premium: can machine learning beat the historical average?

We empirically predict the equity premium with the selected machine learning methods in Gu et al. (Empirical asset pricing via machine learning. Rev. Financ. Stud., 2020, 33(5), 2223–2273). We also consider four additional popular machine learning methods (ridge regression, support vector regression, k-nearest neighbors, and extreme gradient boosted trees) and their combination method. Using a dataset of both macroeconomic and technical predictors, we find that despite showcasing strong in-sample forecasting abilities, particularly with tree-based models, the out-of-sample results support Welch and Goyal (A comprehensive look at the empirical performance of equity premium prediction. Rev. Financ. Stud., 2008, 21(4), 1455–1508) that the competing forecasting models generally fail to outperform the historical average benchmark. We attribute this failure to the small dataset size and the low signal-to-noise ratio inherent in equity premium prediction. Our variable importance analysis further identifies three bond interest rate-related variables as the most dominant predictors for the equity premium. The economic value from a market timing perspective highlights that the historical average benchmark strategy generates the highest average return of 25.63% and the best Sharpe ratio of 0.8. Finally, our findings are robust across a variety of settings.

Social Media Utilization of Microbusinesses in A Component City in Northern Philippines

Social media has become an essential marketing and communication tool for businesses of all sizes, offering opportunities to engage with customers and promote products in a cost-effective manner. This study explored the use of social media by microbusinesses in a component city in the Northern Philippines, focusing on the platforms used, reasons for adoption, benefits gained, and challenges encountered. A qualitative approach was employed, and 30 microbusiness owners from the service, merchandizing, and manufacturing sectors were selected through quota sampling. Data were collected using an interview guide and analyzed using Braun and Clarke’s Thematic Analysis. The results show that Facebook was the most widely used platform, followed by Instagram and TikTok. The primary reason for using social media was to promote products and services. Benefits include wider market reach, increased sales, and improved communication. However, challenges such as market competition and time management difficulties were identified. Despite these hurdles, social media has proved to be a vital tool for business continuity, particularly during a pandemic. The study highlighted the critical role of social media in supporting the growth and sustainability of microbusinesses and recommended further research to explore other social media platforms and business sizes.

Bitcoin Return Prediction: Is It Possible via Stock-to-Flow, Metcalfe’s Law, Technical Analysis, or Market Sentiment?

Popular methods to value Bitcoin include the stock-to-flow model, Metcalfe’s Law, technical analysis, and sentiment-related measures. Within this paper, I test whether such models and variables are predictive of Bitcoin’s returns. I find that the stock-to-flow model predictions and Metcalfe’s Law help to explain Bitcoin’s returns in-sample but have limited to no ability to predict Bitcoin’s returns out-of-sample. In contrast, Bitcoin market sentiment and technical analysis measures are generally unrelated to Bitcoin’s returns in-sample and are poor predictors of Bitcoin’s returns out-of-sample. Despite the poor performance of Bitcoin return predictors within out-of-sample regressions, I demonstrate that a very successful out-of-sample Bitcoin tactical allocation or “market timing” strategy is formed via blending out-of-sample univariate model predictions. This OOS-blended model trading strategy, which algorithmically allocates between Bitcoin and cash (USD), significantly outperforms buying-and-holding or “HODL”ing Bitcoin, boosting CAPM alpha by almost 1300 basis points while also increasing portfolio Sharpe Ratio and Sortino Ratio and dramatically reducing portfolio maximum drawdown relative to buying-and-holding Bitcoin.

Can existing corporate finance theories explain security offerings during the COVID-19 pandemic?

We document substantial increases in corporate security offerings during the COVID pandemic. While the increase in seasoned equity offerings (SEOs) can be attributed to shifts in macroeconomic conditions, increases in convertible and straight bond offerings cannot be explained by standard security choice determinants. We furthermore find that COVID-period SEO announcements are often contaminated with Research and Development (R&D)-related news, with the SEO proceeds more likely to be hoarded as cash. Overall, COVID-period SEOs align with market timing behavior, but the increase in COVID-period convertibles and straight bonds cannot be reconciled with pre-pandemic corporate financing rationales or government interventions. We furthermore demonstrate that the COVID pandemic differs substantially from the Global Financial Crisis (GFC) in terms of security offering choices and announcement returns.

Dependence of green energy markets on big data and other fourth industrial revolution technologies

This paper analyzes the dependence and connectedness among fourth-industrial revolution technology markets (including big data and artificial intelligence, blockchain, and financial technology) and global and regional (US, Europe, and Asia) green energy markets. In particular, we consider the dynamic dependence among these markets in terms of both returns and volatility across different market conditions and investment horizons using the cross-spectral coherence and Quantile-VAR connectedness approach. Three main results emerge from our analysis. First, the return dependence is relatively stronger than volatility dependence and is stronger across most time scales among the technology markets and the European and Asian regional green energy indexes. Second, the return and volatility connectedness is stronger during extreme than normal market conditions. Unless under bullish market times, volatility connectedness appears smaller than return connectedness, implying that market volatility risks spread less forcefully among these markets than return risks under normal and bearish market periods. Third, geopolitical risks, business environment, economic policy, fixed-income, and oil and gold markets’ uncertainties are significant predictors of the degree of return and volatility connectedness. Overall, our findings offer crucial insights for short- and long-term investors interested in portfolios with modern technology and green assets. They also emphasize the roles of market and macroeconomic factors in shock propagation and their implications for low-carbon transition.

Big-Data Technology in Finance: Quality of Stock-Trading Information in Vietnamese Stock Market

Quantitative finance is a field of applied research where the data is important information investors’ profitability and financial risk of financial assets in the processes of decision-making. And financial institutions and securities have applied big-data technologies in stock market to exploit useful information for investors using mobile stock-trading apps. The standardization of database from the raw data of companies’ financial statements, statistical reports and stock-trading data support to enhance data mining analysis by predictive patterns with high feasibility for useful information for investors’ decision-making. And quantitative tools of predictive patterns for financial advanced indexes test expected market return and favourable market timing for buying or selling stocks in stages of market cycle and prediction of market risk before market-trend crash. On the development of securities’ mobile stock-trading apps, the addition of new advanced indexes increases the quality of stock-trading information in Vietnamese stock market generally, seeks competitive advantages of market segments for securities particularly when they change big-data technology in mobile stock-trading apps. This contribution has a better using FinTech combined with stock-trading algorithms to provide more useful and confident information of optimal profitability and financial risk management that individual and institutional investors catch up the trend of big-data technology.

The Parabolic Relationship of Tax Avoidance and Cost of Public Debt

We find that, contrary to prior findings, tax avoidance has a non-linear relationship with cost of debt. Debtholders reward increasing tax avoidance with decreasing bond yields for “undersheltering” firms until reaching a minimum yield. Beyond that minimum yield, as tax avoidance increases, debtholders penalize firms with increasing costs of debt. We use secondary bond market as our empirical setting as the yields are not affected by market timing at issuance. We test several models to conclude that a quadratic function best captures the relation between tax avoidance and cost of debt. Our findings are robust to various specifications of the effective tax rate measure, to the exclusion of large firms, and in both investment grade and speculative grade subsamples.

The market timing ability of bond mutual funds

We apply the nonparametric methodology of Jiang (Journal of Empirical Finance 10:399–425, 2003) to examine whether bond mutual funds can time the bond market by adjusting their portfolios' market exposure based on anticipated market movement. This approach offers several advantages over the widely used regression-based tests such as Treynor and Mazuy (Harvard Business Review 44(4):131–136, 1966) and Henriksson and Merton (The Journal of Business 54(4):513–533, 1981). In a comprehensive study covering the USA, UK, and China, we find some evidence of positive market timing of bond funds at the individual fund level. On average, bond funds show neutral to slightly negative market timing abilities. After controlling for public information, we find that a smaller number of bond funds successfully time the market based on private timing signals. In terms of categories, we find strong evidence of positive market timing for Government bond funds as a group, consistent with the findings of Huang and Wang (Management Science 60:2091–2109, 2014).

A study on the efficacy of an attention mechanism-based strategy planning method for stock trading portfolios

In order to take full advantage of the fact that there is a large amount of information and data available in the field of stock trading, this paper develops a new type of stock trading strategy, which reduces investment risk. A stock trading portfolio strategy planning method based on an attention mechanism, as disclosed in the present invention, relates to the field of stock trading technology. The introduction of an attention mechanism allows the strategy to focus on those stocks with higher evaluation coefficients, thereby improving the selectivity and accuracy of the strategy and thus the effectiveness of the portfolio strategy. Furthermore, based on the prediction results of the deep spatio-temporal neural network model, it is possible to determine the stock's short-term trends and identify the optimal trading points. This information can be utilized as a reference for investors, assisting them in making more informed decisions regarding the timing of buying and selling, improving the success rate and yield of trading, enabling them to swiftly and accurately assess the performance of each stock, facilitating timely adjustments to their portfolios, and enabling them to devise the optimal trading strategy and timing for each stock, enhancing the accuracy and efficiency of their trading decisions.

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.

Determinants of IPO Overpricing

AbstractThis paper outlines the phenomenon of negative first‐day IPO returns. Using a comprehensive sample of IPOs in the United States between 2000 and 2020, we find that 21.61% exhibit negative first‐day returns, making this a common feature of the US IPO market. The key findings show that the IPO mechanism is important. A larger deal size and proportion of over‐allotment shares reduces the probability of IPO overpricing, while downward offer price adjustments increase the likelihood of negative first‐day returns. Despite distinct differences, the analysis reveals shared characteristics between IPO underpricing and overpricing, providing nuanced insights into IPO pricing. Neither market timing nor agency issues significantly affect IPO overpricing.

CPTPP State-Owned Enterprise Rules and Our Countermeasures

State-owned enterprises (soes) play an important role in the global economy. Some developed countries believe that because of the state nature of SOES, SOES enjoy more privileges and unfair competitive advantages than private enterprises, including government subsidies and regulatory incentives, so that their market competitiveness and overall strength are strong. In order to get rid of the “Official business” advantage of state-owned enterprises, promote the equal competition between state-owned enterprises and private enterprises, and reduce the concern of state-owned enterprises, the rules of our state-owned enterprises are gradually formed. For the first time in the international market, CPTPP specifically regulates the rules of state-owned enterprises. Compared with other free trade agreements, cPTPP's SOE rules are characterized by stronger competition neutrality rules, wider scope of application, more non-commercial aid rules and higher transparency. In the context of China's imminent accession to CPTPP, this paper analyzes the regulation of our state-owned enterprises that have signed the free trade agreement, and finds that the number of state-owned enterprises to be regulated in the free trade agreement is small and the level of regulation is low. Therefore, we must actively negotiate the new issues of state-owned enterprises and actively participate in the construction of international rules of state-owned enterprises. For the CPTPP high-level SOE rules negotiation, we should follow China's national conditions, study the introduction of ownership-neutral competition-neutral principle as the core, and further deepen the reform of state-owned enterprises, state-owned enterprises to standardize the way of subsidies.

Assessing Mutual Fund Performance in China: A Sector Weight-Based Approach

In many financial markets across the globe, full historical position disclosure is not required of mutual funds, or it is subject to prolonged delays, often due to regulatory restrictions. This makes measuring fund manager performance based upon the stock-picking and market-timing skills from past literatures impossible. This study introduces a new methodology utilizing sector weight analysis to estimate the stock-picking and market timing skills of 198 Chinese equity mutual fund managers. Within-sample predictions confirm that the new measures are robust and reliably identify fund managers who outperform their peers, suggesting that this method may be useful in other institutional settings where the full historical position of funds is unavailable. Fund managers with lower stock picking or market timing skills are more likely to improve their skills in the following period, which suggests that manager skills develop and change over time. Finally, our analysis reveals that fund managers with higher stock picking skills are significantly less likely to be replaced, thereby enjoying greater job security.