This study investigates the dynamic connectedness among six key sectors in the Australian Stock Exchange (ASX) using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. By examining the interactions among Consumer Staples, Energy, Financials, Industrials, Information Technology, and Metals & Mining indices, the analysis highlights how sectoral connectedness evolves, particularly during periods of economic crisis. The results reveal that specific sectors act as net transmitters or receivers of shocks. Energy, Metals, & Mining are more sensitive to global commodity prices, while Consumer Staples maintain stability. This approach offers a comprehensive view of sectoral risk transmission and its implications for market stability and risk management. The findings provide critical insights for investors and policymakers aiming to mitigate systemic risks and enhance portfolio diversification in response to market fluctuations.

ABSTRACTAlthough there is broad consensus on a robust momentum effect in Australia, the interaction between momentum and capital structure has been underexplored in the literature. This paper explicitly examines whether capital structure promotes momentum trading in the Australian stock market. The data sample includes over 1800 stocks listed on the Australian Stock Exchange from 2000 to 2023. We construct momentum portfolios using the monthly rolling and overlapping techniques. Two ratios are calculated to measure the firms’ capital structure: the book‐value and market‐value financial leverages. Irrespective of the capital structure measure, the superior returns of the Winner quintile are concentrated in highly leveraged stocks. In contrast, the high‐leverage Loser performs worst among the Loser quintile. The return of momentum strategy enhanced with capital structure is more than 1.5 times the original momentum profit. The risk‐adjusted analysis paints a similar return pattern. Additionally, we observe high volatility in earnings and cash flows for highly leveraged stocks, leading to significant mispricing. Thus, the interaction between momentum and capital structure may stem from increased misvaluation, consistent with a behavioral explanation.

ABSTRACTThis article proposed a novel hybrid framework, the WTC‐DCA‐Informer, for forecasting volatility in the Australian stock market. The findings indicated that (1) through a comprehensive comparison with various machine learning and deep learning models, the proposed WTC‐DCA‐Informer framework significantly outperformed traditional methods in terms of predictive performance. (2) Across different training set proportions, the WTC‐DCA‐Informer model demonstrated exceptional forecasting capabilities, achieving a coefficient of determination (R2) as high as 0.9216 and a mean absolute percentage error (MAPE) as low as 13.6947%. (3) The model exhibited strong adaptability and robustness in responding to significant market fluctuations and structural changes before and after the outbreak of COVID‐19. This study offers a new perspective and tool for forecasting financial market volatility, with substantial theoretical and practical implications for enhancing the efficiency and stability of financial markets.

This study explores how uncertainties in domestic financial markets, including stocks, bonds, and exchange rates, as well as global crises like the subprime mortgage and European sovereign debt crises, affect the dynamic correlation between the Australian stock market index (ASX 300) and foreign exchange rates. Using quantile regression estimation and analyzing high-frequency data from 1999 to 2021, we uncover distinct relationships influenced by currency-specific uncertainties. Our findings reveal that Australian stock market concerns impact co-movements with financial instruments, showing different effects on the correlations with the Euro, British Pound, US Dollar, Japanese Yen, and Chinese Yuan. Surprisingly, uncertainties in the Australian bond market have a negative impact on co-movement with the Euro and British Pound but a positive impact with the US Dollar, Japanese Yen, and Chinese Yuan. Additionally, we observe that volatility in the Australian currency’s exchange rate with various currencies positively influences dynamic co-movements. However, the strength of this connection varies based on the volatility of the Australian dollar against the Japanese Yen. Global financial crises, especially the subprime mortgage crisis, significantly impact dynamic co-movements, supporting both Flow and Stock-oriented theories. In summary, our research sheds light on the diverse impacts of domestic financial market uncertainties on co-movement, providing valuable insights for portfolio managers and foreign investors aiming to understand the intricate relationship between the Australian stock market and exchange rates. JEL Classification: C32, F31, G01, G15

The Impact of the First Six Months of Trump's Presidency on the Australian Stock Market

<p>The purpose of this study is to investigate the causality relationship among the GEPU (Global Economic Political Uncertainty) index and the stock market index of the MIKTA countries. Accordingly, Hatemi-J (2012) asymmetric causality test was applied to investigate the existence of a relationship between the stock markets of the MIKTA countries and the GEPU index. In the study using monthly data, the period between 1999 and 2022, which is the widest data range for all variables, was taken into consideration. First of all, Lee-Strazicich unit root test was used to test the stationarity of the variables and it was observed that the variables were stationary at different levels. Then, the GEPU index is taken as the dependent variable and models are constructed as paired tests for each MIKTA country stock market. (Walmex) for Mexico, (Jakarta45) for Indonesia, (Kospi200) for South Korea, (BIST100) for Türkiye, and (ASX) for Australia are taken as the representative indices of MIKTA country stock markets. The results of the study show that there is a statistically significant causality effect of the GEPU index on stock markets. In general, a negative change in the GEPU index is found to be more dominant on stock markets compared to a positive change. On a country basis, it is found that an increase in the GEPU index causes a decrease in the Mexican, South Korean and Turkish stock markets. In addition, the lack of causality effect in the Australian stock market is interpreted as the fact that this stock market moves more independently from this index.</p>

The prediction of stock market movements is a critical task for investors, financial analysts, and researchers. In recent years, significant advancements have been made in the field of stock prediction, driven by the integration of machine learning and data analysis techniques. Though stock market predictions are highly desired, there are many factors contributing towards volatility of the market. There is a need for extensive study and concentration on various predictive techniques to investigate different scenarios triggering such volatility. This paper reviews the latest methodologies employed for predicting stock prices, with a particular focus on the Australian stock market. Key techniques such as time series analysis like ARIMA & GARCH, machine learning models like SVM, LSTM & Neural Network, and sentiment analysis are discussed, highlighting their applications, key strengths, and some limitations.

The future of portfolio management is evolving from relying on human expertise to incorporating artificial intelligence techniques. Traditional techniques such as fundamental and technical analysis will eventually be replaced by more sophisticated deep reinforcement learning (DRL) algorithms. However, it is still a long way from designing a profitable strategy in the complex and dynamic stock market. While previous studies have focused on the American stock market, this paper applies two DRL algorithms, the proximal policy optimization (PPO) and the advantage actor–critic (A2C), to trade the constituent stocks of the Australian Securities Exchange 50 (ASX50) Index. This paper also incorporates a weighted moving average into the action space and introduces a transaction threshold to help agents minimize trivial trades that lead to high transaction costs. The results are presented and benchmarked against the ASX50 Index. The A2C agent was better at following trends and had the higher upside potential but can suffer from more severe damage during bearish markets. On the other hand, the PPO agent had the lowest annual volatility and the highest maximum drawdown, which is more helpful in a bearish or volatile market.

We use stock-day level data on the market share of algorithmic trading to analyze whether algorithmic trading affects the frequency of mini flash crashes in the Austrian stock market. We use an instrumental variables approach and the Petrin and Train (2010) control function approach to address endogeneity concerns. We find no evidence that algorithmic trading significantly affects the probability of the occurrence of mini flash crashes.

Asymmetric shocks of the COVID-19 pandemic on the Australian stock market: Evidence from multiple threshold nonlinear ARDL (MTNARDL) approach

For many years it has been predicted that group practice of Maharishi’s Transcendental Meditation and TM-Sidhi program alleviates social stress and that this phenomenon is measurable using specific social indicators. This study examines this prediction in two parts: Part I: To test the prediction in an Australian context; and Part II: To relate the significance of the outcomes of Part I to future directions of social work for the reduction of disorder and suffering in society. The test period in Part I was January 1983 when the size of a group of participants in Maharishi’s Transcendental Meditation and TM-Sidhi program, including Yogic Flying, reached the √1% of the population of Australia. Using a Box-Jenkins (ARIMA) time-series analysis, it found there was a significant improvement in the quality of national life in Australia as measured by reduced fatal traffic accidents, reduced unemployment, and an increased value of the main Australian stock market index. The results can be summarised as follows: a) Highly significant decreases (14.0%) in fatal traffic accidents, p = 0.0005; b) Highly significant decreases (8.3%) in unemployment, p = 0.0005; and Significant increases in the Sydney-Melbourne All Ordinaries Stock Exchange Index, p = 0.025.

ABSTRACT This study examines the explanatory power of the CAPM and downside risk asset pricing models (the downside beta and the realized semibeta models) for the next-month firm-level cross-sectional stock return variation in the Australian stock market. We show that the CAPM beta, downside beta, semibeta BetaNP, and semibeta BetaNN negatively predict future stock returns, which is inconsistent with the findings in the original study by Bollerslev, Patton, and Quaedvlieg (2022). The BetaNN measures the individual stock movement in the same direction as the downward stock market, while BetaNP measures individual stock downward with the upward stock market. These findings are robust, not subsumed by conventional cross-sectional asset pricing factors, and consistent with the existing Australian downside risk study.

An empirical evaluation of the salience-based asset pricing model: Evidence from Australia

Abstract Liquidity is an important characteristic of financial markets, affecting portfolio decisions and priced risk. During periods of market turmoil, such as occurs during financial crisis, investors have an elevated need for cash and so understanding how liquidity differs during those periods is important. We examine how stock market liquidity was impacted by two crises with distinct origins, the global financial crisis (GFC) and the COVID‐pandemic. Our sample includes the S&P/ASX200 constituents for the period January 2005–December 2020. We find that the Australian stock market is less liquid during both crisis periods; spreads are wider, depth is lower, and price impact is larger (stock prices move a lot in response to small amounts of volume). Although the magnitude of the liquidity change is greater at the onset of COVID, the duration of the impact is longer during the GFC, resulting in a larger average effect. While trading volume declines during the GFC, it increases during COVID. Our results are robust to alternate liquidity proxies, methodologies and crisis period identification, and generally applicable across stock sectors.

The Australian stock market is discussed in this report because of its importance as a financial hub. In Australia, the primary market operator is the Australian Securities Exchange Ltd (ASX), which is increasingly utilizing technology to develop novel approaches to maximizing shareholder value. Several aspects of the Australian stock market and its participants are discussed using the existing literature as a basis for this paper. The report provides context for ASX's technology deployment. Secondly, it draws attention to the ASX's significant participants. The article's third section discusses the opportunities and threats that participants in the Australian stock market face. Problems have arisen in the regulation and operations of the Australian stock market due to recent changes, such as the transfer of regulatory authority to ASIC, the introduction of new rules regarding the disclosure of information regarding securities lending and short sales, and the introduction of novel market integrity rules. Investing in Australia's financial markets makes sense for several reasons, including the country's rapidly growing domestic market, sophisticated corporate infrastructure, pension-friendly government, and highly educated, multilingual workforce.

Foreign institutions, local investors and momentum trading

PurposeThis study explores the economic impact of the COVID-19 crisis on herding behaviour in the Australian equity market by considering liquidity, government interventions and sentiment contagion.Design/methodology/approachThis study utilizes a daily dataset of the top 500 stocks in the Australian market from January 2009 to December 2021. Both predictive regression and portfolio approaches are employed to consider the impact of COVID-19 on herding intention.FindingsThis study confirms that herding propensity is more pronounced at the beginning of the crisis and becomes less significant towards later phases when reverse herding is more visible. Investors herd more toward sectors with less available information on financial support from the government during the financial meltdown. Conditioning the stock liquidity, herding is only detectable during highly liquid periods and high-liquid stocks, which is more observable during the initial phases of the crisis. Further, the mood contagion from the United States (US) market to Australian market and asymmetric herding intention are evident during the pandemic.Originality/valueThis is the first study to shed further light on the impact of a health crisis on the trading behaviour of Australian investors, which is driven by liquidity, public information and sentiment. Notwithstanding the theoretical contributions to the prior literature, several practical implications are proposed for businesses, policymakers and investors during uncertainty periods.

Abstract This paper investigates a set of realised higher‐order co‐moment risk–return relationships in the Australian stock market. We test the predictive power of the asset pricing model by implementing the two‐, three‐, four‐moment Capital Asset Pricing Model. Our findings show that investors respond differently to information related to realised higher‐order co‐moments, and that the corresponding gamma (normalised co‐skewness) and kappa (normalised co‐kurtosis) risk factors remain priced in the presence of continuous beta and jump beta. Furthermore, we find that the realised high‐order co‐moment risk measures are priced differently and remain significant even when combined with a set of firm characteristics.

The impact of the Russia-Ukraine crisis on the stock market: Evidence from Australia

Investor sentiment and stock market anomalies in Australia