The Journal of Prediction Markets

The Effect of Firm-Specific and Industry-Specific Determinants on Automobile Industry Export Performance: The Mediating Role of Supply Chain Performance

Saswati Tripathi — 2025-10-04

This paper analyzes the effect of firm-specific (FSD) and industry-specific determinants (ISD) on supply-chain-performance (SCP), export performance (EP) and SCP’s mediating effect on the relationship between FSD, ISD, and EP. It develops a theoretical framework from literature and empirically validates using the Indian automobile industry segments (IAIS) data. The sample frame consists of firms in ISIS between 2010–11 and 2020–21. The paper employs factor analysis for construct validity, panel-data-fixed-effect models to analyze the relationships, and bootstrap for cross-validation. It reveals that FSD and ISD directly influence both SCP and EP. SCP completely mediates the relationship between FSD, ISD, and EP.

ARIMA and Exponential Smoothing Models in Forecasting the Macau Property Price Index During COVID-19

Simon M. S. So — 2025-10-04

The real estate market plays an important role in the economies of many countries, and the future trend of the market has long been a topic of concern to both academics and practitioners. This paper attempts to study the effectiveness and superiority of two univariate time series models, Autoregressive Integrated Moving Average (ARIMA) model and exponential smoothing, to forecast the Macau residential property price index during the COVID-19 pandemic. Based on 1- and 2-year holdout samples during the shock of COVID-19 in Macau, the results show that the out-of-sample forecasting performances of both models are better than the baseline model of classical decomposition. There is also evidence that the ARIMA models outperform the Winters three-parameter exponential smoothing models in the two out-of-sample periods. Therefore, in the context of unprecedented events such as COVID-19, the ARIMA method is more effective than the Winters exponential smoothing method in making rapid and accurate adjustments when the Macau residential property price index is significantly affected. Our findings provide important implications for relevant government departments, home buyers and sellers, and property market participants in their selections of reliable models to forecast future property market behavior.

Price Prediction of S&P 500 Using Machine Learning

Manel Dahmani — 2025-10-04

Forecasting trends in stock indices is considered a difficult task in financial time series forecasting. Accurate forecasts of stock price trends can generate profits for investors. Due to the complexity of stock market data, developing effective forecasting models is very challenging. We are trying to predict stock prices for the next few days. This will become the basis for knowing the right time to invest or exit positions and generate profits.

With the introduction of artificial intelligence and the increase in computing capacity, programmed forecasting methods have proven to be more effective at predicting stock prices. In this work, we used supervised machine learning algorithms such as linear regression model, SVR, XGBoost, and random forest.

Thus, these models are evaluated using standard strategic indicators such as the EMR. A low value of the indicator shows that the models are effective in predicting stock prices.

Predicting Motor-Vehicle Deaths Using Machine Learning: Proposed 8E-Model

Saurabh Kumar — 2025-10-04

This study conducts a comprehensive time series analysis of motor-vehicle fatalities in the USA spanning from 2019 to 2021, revealing a troubling upward trajectory. Factors such as over-speeding, impaired driving, reduced road traffic enforcement during the pandemic, and instances of driving under the influence have significantly contributed to the surge in fatal crashes during this period. Utilizing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, this research forecasts the trajectory of motor vehicle deaths in the USA. The forecast suggests a continuation of the upward trend, emphasizing the urgency of addressing the escalating fatalities. In response to the burgeoning global trend of increasing accidents and fatalities, this study advocates for the implementation of broader preventive measures worldwide. Proposed strategies encompass the crucial role of policy implementation and road safety measures in curbing the rising toll of road accidents, particularly in the USA.
Furthermore, this study extends the existing 7E model (Education, Engineering, Enforcement, Exposure, Examination of Competence and Fitness, Emergency Response, and Evaluation) by introducing the eighth ‘E’—Empathy—in the context of road safety. This augmentation creates the 8E model, offering a more encompassing framework adaptable on a global scale. The inclusion of empathy underscores the significance of considering human emotions, behaviours, and societal impact in crafting effective road safety initiatives.

Volatility Spillovers Between Financial Markets and Cryptocurrencies

Lamia Sebai — 2025-10-04

This paper analyses the relationships between the volatilities of five major stock markets (S&P 500, CAC 40, DAX, FTSE 100, and Nikkei 225) and five cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, and Ripple), (WTI), and gold. The GARCH model, which describes the volatility of financial assets and cryptocurrencies, was used. A significant and higher volatility spillover was observed across these market pairs. The conditional correlation between Bitcoin and other cryptocurrencies is time-varying, but the conditional correlations between crypto-currencies and gold and all assets are negative during the period (2017-2018) and positive. At the beginning of the COVID-19 crisis, the conditional correlation between cryptocurrencies, stock indices, and WTI increased, which confirms the impact of COVID-19 related contagion between them.
Our findings show that cryptocurencies and gold are considered hedges for the international investors during the period 2017-2018.