Market Timing Research Articles

Time-aware forecasting of search volume categories and actual purchase

The new e-commerce field has attracted businesses of all sizes, retailers, and individuals. Consequently, there is an ongoing necessity for applications that can offer predictions on trending products and optimal selling time. This research suggests aiding businesses in forecasting demand for various product categories by employing data mining algorithms on multivariate time series data. To ensure the most recent information, real-time data was gathered through APIs to build the first block in this research. While search volume was derived from the Keywords Everywhere tool, Amazon's search volume was derived from the Helium 10 tool and external features about actual purchased data. The harvested raw datasets went through multiple processes to generate the dataset and were validated. The models XGBoost, Linear Regression, Random Forest, long-short-term memory, and K-nearest neighbor were employed to predict the trends, and the performance is demonstrated using evaluation metrics, namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Overall, Linear Regression outperformed, especially at a correlation coefficient of 0.9, with R2 = 90.688, MAE = 0.038, MSE = 0.003, and RMSE = 0.057. KNN outperformed on correlation coefficient of 0.7, R2 = 85.129, MAE = 0.045, MSE = 0.005, and RMSE = 0.068. XGBoost produced the best results with a correlation coefficient of 0.9, yielding R2 = 85.89, MAE = 0.042, MSE = 0.004, and RMSE = 0.062. Random Forest, on the other hand, achieves peak metrics with a correlation coefficient of 0.6, R2 = 84.854, MAE = 0.041, MSE = 0.004, and RMSE = 0.066.

Does ESG affect stock market dependence? An empirical exploration of S&P 1200 companies shows the divergent nature of E–S–G pillars

If ESG characteristics of companies attract loyal and resilient investors and customers, does ESG affect the stock-market connectedness during market upturns and downturns? The micro-level analysis uses the cross-quantilogram to measure quantile dependence between stocks in the S&P 1200 and the market with distinguishing factors of ESG scores. We employed the scores of seven major ESG data providers and their structural combination to capture the shared information. We found a systematic effect both on the level of multidimensional and unidimensional ESG scores. Moreover, we showed Governance scores create a counter-effect on the Environmental and Social pillars. In general, the high- and middle-ESG stocks tend to have lower dependence on the market during market downturns, with E and S scores pushing the dependence down and the G dimension pushing it up. Results suggest a lower distinguishing power of Environment and Social pillars and persistent Governance pillar power during moderate and upturn market times.

Decreasing Waiting Time for Outpatient Chronic Prescriptions Pickup Using Drive through 24/7 Automated Dispensing Carousel System at King Salman Military Hospital (KSAFH)

Extended waiting periods have an effect on healthcare accessibility and delivery. The main purpose of drive through 24/7 automated dispensing carousel system is reducing the necessity for patient to visit the hospital to dispense his/her chronic medications, increasing patient's convenience and relieving congested parking spaces in the hospital. Several methods have been implemented in other countries to reduce patient waiting time. Some of which include automated dispensing system, staff scheduling plan, postal-delivery prescription, and redesign of pharmacy workflow [1-4]. In SAUDI ARABIA, one of the methods adopted was implementation of drive through 24/7 automated dispensing carousel system (DAD). DAD was developed to make patients' chronic prescription pickups easier. The hospital has implemented Capsa Healthcare’s RoboWall as a drive-through model for the first time in the global market at King Salman Armed Forces Hospital (KSAFH) to facilitate the convenient delivery of prescription drugs to patients. DAD is a collection of innovative dispensing systems that offers patients new concepts for dispensing their prescription. Pharmacy tech workstations generally have barcode-driven internal workstations, flat screen displays facing patients, Robowall software, and SMS (text messaging) modules. This project will assess were to (1) assess the effectiveness of drive through 24/7 automated dispensing carousel system in improving waiting time for outpatient chronic prescriptions pickup and determine the feasibility of this system in a busy tertiary military hospital. Data will be extracted from routinely queuing system, hospital information systems, and patient experience. The aim of this study will focus on and explore the current practice and its barriers to waiting time for outpatient chronic prescriptions pickup. Where possible, generalize the study to other military hospitals among the Ministry of Defense and other health care providers at Saudi Arabia.

Market Timing, Selectivity, and Performance of Real Estate Mutual Funds

Market Timing, Selectivity, and Performance of Real Estate Mutual Funds

Research on the Current Situation of Quantitative Investment in China

Quantitative investment, as one of the many tools in the investment toolkit, plays a significant role in investment practice. Quantitative investment strategies encompass quantitative industry allocation, stock selection, and market timing strategies, among others. In the highly developed capital markets of the United States, quantitative investment has a history spanning several decades. It has garnered praise from numerous corporations and individual investors for its stable investment returns and rational investment style, making it an essential decision-making factor for other investors as well. In China, quantitative investment is still in its nascent stages. This article analyzes several issues existing in the Chinese quantitative investment landscape, such as limited hedging mechanisms for investment strategies, some strategies having a significant impact on the market environment, and the incompatibility of the unique Chinese financial market environment with traditional quantitative investment strategies. The article proposes corresponding solutions to these problems, which hold positive implications for further improving China's financial markets.

Valuation effect of ESG and its impact on capital structure: Evidence from Europe

In this study, we examine the valuation effect of ESG performance using a sample of 895 European companies. The misvaluation of the company is determined by the ratio of its market price to its true value. A true value calculation is based on two measures: first, on the analysts' forecasted price, and second, on residual income. We find that improvements in ESG profiles increase market prices in relation to their true value. An analysis of overvalued and undervalued stocks separately revealed that ESG performance further enhances the existing level of overvaluation. Conversely, it restores undervalued stocks to their true value for residual income measures of misvaluation. Alternatively, mispricing measured by analysts' forecasted price is significant only for undervalued stocks and the entire sample. Our analysis suggests that information asymmetry and market sentiment play a moderating role in the ESG-misvaluation nexus, suggesting that ESG is a friction to market efficiency. ESG-related misvaluations further impact capital structure through market timing practices, and the increased stability of CSR can be attributed to a marginal increase in equity issuance. We attribute this valuation effect to the demand effect associated with ESG investments. The findings are robust to alternative measures of estimation.

Carbon price prediction based on a scaled PCA approach.

Carbon price prediction is of great importance to regulators and participants in the carbon trading market. It is the basis for developing policies related to the carbon trading market and stabilizing that market. Considering the numerous factors that influence carbon prices in China, dimensionality reduction is needed to improve the prediction accuracy and efficiency. However, the traditional dimensionality reduction methods fail to fully consider the role of influencing factors, which has certain limitations. In this paper, a new dimensionality reduction method, namely scaled principal component analysis (s-PCA), is employed to improve the prediction accuracy of carbon prices. Firstly, a factor library that influence carbon prices is constructed from three perspectives: technical indicators, financial indicators and commodities indicators. Then, the s-PCA method is used to reduce the dimensionality of factors influencing carbon price. Next, two different methods are used to predict carbon prices, including traditional regression method and Long Short-Term Memory (LSTM) method. Finally, the economic value of the s-PCA method is examined by constructing investment portfolios. The empirical results of the Hubei Emissions Exchange show that the s-PCA model outperforms other competing models both in- and out-of-sample. In addition, the LSTM model could improve the performance of the s-PCA model in carbon price prediction. From a market timing perspective, investors can achieve a greater return and a larger Sharpe ratio using the s-PCA method than using other comparative methods and buy-and-hold strategy. Therefore, the s-PCA method is effective and robust in predicting carbon price.

Foreign Institutional Investors, Controlling Shareholders, and Market Timing: Evidence from India

Foreign Institutional Investors, Controlling Shareholders, and Market Timing: Evidence from India

Market timing and predictable profitability

Market timing and predictable profitability

Women's Waiting Time in Labor Market: Evidence From Iran

Women's Waiting Time in Labor Market: Evidence From Iran

A novel nomogram for predicting endoscopic remission in refractory Crohn's disease with ustekinumab administration.

Ustekinumab (UST) is a human IgG1 monoclonal antibody that targets to the share p40 subunit of interleukin-12(IL-12) and IL-23. Evidence has shown that UST therapy is well tolerated and effective in inducing clinical response in refractory CD(Crohn's disease) and dose escalation is effective in recapturing response in over half of the patients. However, no predictive factor has been reported to be helpful for UST treatment in clinical practice. Additionally, there were few reports about therapeutic drug monitoring (TDM) of UST administration in China due to its late launch time in Chinese market and lack of experience in clinical use. Herein, we establish and validate the first UST-trough concentrations (TCs) -related nomogram in China for predicting endoscopic remission in refractory CD to facilitate clinical decision making.

StockBiLSTM: Utilizing an Efficient Deep Learning Approach for Forecasting Stock Market Time Series Data

StockBiLSTM: Utilizing an Efficient Deep Learning Approach for Forecasting Stock Market Time Series Data

Addressing stock market time series trends and volatility using optimised DE-LSTM model

Addressing stock market time series trends and volatility using optimised DE-LSTM model

A Neural Network Architecture for Maximizing Alpha in a Market Timing Investment Strategy

A Neural Network Architecture for Maximizing Alpha in a Market Timing Investment Strategy

Long Run Performance of Rights Issues and FPOs: Evidence from India

In this article, we study the long run equity performance of Indian firms which issued Seasoned Equity Offering (SEO) during the period January 2010–December 2022. With a sample of 177 such SEO issuing firms, we show that the long run equity performance of these SEO issuing Indian firms have been significantly inferior compared to firms from the same industry which are similar w.r.t other parameters like size, age and price-book ratio. Investing in an SEO at the close of the first trading day and holding it for 5 years, on an average, gave 17% (3% on an annualised basis) fewer returns than comparable firms. This pattern is more or less independent of the market conditions (bull or bear) except for a brief period during the global financial crisis. The study’s findings suggest that Indian firms that issue SEOs may be underperforming their industry peers over the long term. This could be due to a number of factors, such as the companies raising capital for dilutive purposes, using the proceeds to invest in underperforming projects, or facing challenges in integrating new equity into their existing business operations. Additionally, the study’s findings highlight the importance of careful consideration of long-term performance when evaluating investment opportunities in SEOs. To further explore the factors contributing to the underperformance of SEO-issuing firms, future research could investigate the specific reasons for their lower returns, analyze the impact of different types of SEOs (e.g., rights issues vs. follow-on public offerings) on long-term performance, and examine the role of market timing and investor sentiment in influencing the performance of SEOs.

Market Timing Investment Methods on the Budapest Stock Exchange

Market Timing Investment Methods on the Budapest Stock Exchange

The Art of Timing: Unpacking the Market Timing's Long -Term Impact on Capital Structure : With Reference to Listed Companies in Colombo Stock Exchange

The market timing theory serves as the basis for this study, which aims to unravel the persistent reaction to capital structure decisions. The rationale for conducting this study stemmed from observing a gap in understanding the capital structure behaviour of firms surrounding their initial public offerings and the progression of debt and equity from that initial point. The study's sample, which covered the years 1992 to 2018, was created by using the IPO date of companies listed at the Colombo Stock Exchange) in Sri Lanka. To measure market timing, the study utilized a variable called external finance weighted average market-to-book ratio. This variable served as an indicator of the firms' timing decisions in relation to the capital structure. A panel regression model quantified the timing impact, showing an inverse association between variables. However, the strength of the correlation was found to be less significant than what market timing theory had predicted. Thus, it could not be proven that listed companies consistently increase their equity by issuing new shares when the current share price rises. These findings suggest that while market timing may have some influence on the determination of the debt and equity portions in capital structure decisions, other factors may also come into play. The study highlights the importance of considering additional variables and factors that can impact capital structure choices. It further emphasizes the significance of increasing equity on firm capital structures and costs, and suggests that management can potentially adjust the capital structure based on historical market-to-book ratios.

Market Timing and Asymmetric Information: How do Rights Issues and Private Placements Attract Investors to Overvalued Stocks?

Manuscript type: Research paper Research aims: This study examines the background of selecting the equity offering mechanism between rights issues and private placements when facing asymmetric information and overvalued prices. Design/Methodology/Approach: This research employs the concept of information asymmetry and focuses on analysing market timing data about stock offering transactions in Indonesia from 2000 to 2020. This study uses regression analysis to examine the correlation between information asymmetry and trade volume. Abnormal trade volume before various offerings is subjected to regression analysis using generally used proxies for information asymmetry. Research findings: The conclusions of our research indicate that companies that issue more shares than the overpriced stock tend to exhibit a more excellent abnormal return value in the context of rights issues. The sales volume indicates the company’s prospects derived from private information obtained during the offering Theoretical contribution/Originality: There is a shortage of existing literature about research that specifically investigates the identification of SEO techniques that elicit negative market responses. Consequently, this study explores the connection between the underlying motivations driving the selection of stock offering mechanisms. Practitioner/Policy implication: The distribution of profits in the transaction indicates the company’s future policies. Before investing, investors can acquire pertinent information on the company’s current state, strategic goals, and prospects. Research limitation: The present analysis has not considered the factors of internal ownership or insider trading. These factors might serve as a foundation for investigating the underlying incentives for issuing shares with more precise market timing considerations.

Dual-Attention Based Multi-Path Approach for Intensifying Stock Market Forecasting

In light of the existing challenges in capturing short-term fluctuations and achieving accurate predictions in stock market time series data, this research presents the “Dual-Attention MDWT-CVT-LSTM,” a revolutionary financial time series forecasting model. This model makes use of a dual-attention mechanism in conjunction with a Variant Transformation-Gated Long Short-Term Memory (LSTM) network. The approach relies on the Multilevel Discrete Wavelet Transform (MDWT) to separate stock market index sequences into high-frequency and low-frequency components. Furthermore, a transformative gating mechanism is incorporated into the LSTM, featuring fusion gated units to create the VT-LSTM, effectively addressing abrupt short-term information changes. Within the dual-attention network, VT-LSTM is combined with one-dimensional temporal convolution (Conv1D) to extract spatial and temporal features of data at different frequencies. This facilitates the prediction of various sub-sequences, enabling multilevel and multi-path forecasting research. The experimental evaluation uses diverse models on stock market index datasets and individual stock datasets. The results show the superior prediction accuracy of the proposed model compared to alternative methods, underscoring its practical viability in stock market forecasting applications.

A Critical Review of the Effects of Stock Returns and Market Timing on Capital Structure

A Critical Review of the Effects of Stock Returns and Market Timing on Capital Structure