Trading Intentions Research Articles

Examining market volatility arbitrage in cryptocurrencies with the perspective of Beldex coin trading dynamics in India

Cryptocurrency trading has gained significant adhesion in financial markets, making it essential to understand the factors influencing trading intentions. This study investigates the psychological and knowledge-based determinants of trading intentions towards Beldex coins among crypto traders in India. This study aims to evaluate how risk management, hedonic motivation, investment desire, market knowledge, peer participation, and earning desires impact trading intentions. A survey was conducted with 369 crypto traders in India, and multiple regression analysis was employed to analyze the data. The results indicate that all six factors significantly influence trading intentions, with risk management (β = 0.342, p < 0.001) and earning desires (β = 0.378, p < 0.001) having the strongest impact on Indian Cryptocurrency market arbitrage. The regression model explained 53% of the variance in trading intentions (R² = 0.53). Cryptocurrency market information is analyzed through the CoinGecko tool that provides charts, market capitalization, and blockchain data; multiple regression analysis is utilized to test the hypothesized relationships. This study reveals that traders’ investment decisions in cryptocurrencies are primarily driven by financial motivations, including potential high returns, diversification, and inflation hedging, as well as technological factors of decentralized finance, blockchain technology, and digitalized transactions. AcknowledgmentThe authors would like to convey their gratitude to Prof. Balakumar Pitchai, Director/Research, Training & Publications at the Office of Research & Development, Periyar Maniammai Institute of Science & Technology (Deemed to be University), India for his suggestions to improve the language of the manuscript.

Optimal liquidation using extended trading close for multiple trading days

The extended trading close (ETC) provides institutional investors an opportunity to trade at the closing price after the regular trading session (RTS) and disclosing the order imbalances to other market participants. ETCs exist in the Nasdaq, the SSE STAR, the SZSE ChiNext and the TWSE. To help a risk-averse institutional investor take advantage of the RTS and the ETC for liquidation, we develop a multistage dynamic programming model including the ETC, and derive recursive solutions for the multiple trading days scenario with closed-form solutions for the scenario with only two trading days. We also verify that the ETC is able to mitigate extreme price movements caused by fast liquidation, which is also a goal of the ETC set out by the SSE STAR and the SZSE ChiNext. Finally, we derive three results. First, an institutional investor can reduce execution costs after the introduction of the ETC. Second, a critical trading day exists, and to avoid prematurely revealing trading intentions, the investor should not trade in the ETC until such day. Third, even though the ETC orders submitted by the investor are unfilled, implementation of the ETC encourages the investor to change the liquidation strategy in the RTS, which reduces extreme price movements. In summary, the practical implications of this paper are that the investor should not trade during the ETC on the front few days to avoid prematurely revealing the investor’s trading intention by unfilled orders in the ETC and that introducing the ETC can reduce liquidation costs and extreme price movements.

Retail Investor Trading Intentions: New Evidence from Australia*

Using a quarterly survey of 23,000 Australian retail equity investors, we study the relationship between beliefs about risk and returns and stock market intentions regarding holding, buying and selling. We provide evidence that investor intentions regarding buying or selling are primarily characterised by risk and return expectations, but sensitivity to their beliefs about risk and returns is greater for the intention to sell than to buy. In contrast, demographic factors such as gender, income and age play a key role in the decision to hold rather than trade. The results have significant ramifications for stock market participation and trading intensity in Australia.

Advance disclosure of insider transactions: Empirical evidence from the Vietnamese stock market

We investigate the impact of legal insider trading announcements on stock returns in Vietnam. Vietnamese insiders must announce their trading intentions in advance as well as after the actual trade, which contrasts with the vast majority of countries where only post-trade announcement is mandatory. This regulatory setting allows to put to test the predictions from the theoretical models of advance disclosures (Huddart et al., 2004, Lenkey, 2014). Consistent with the theoretical literature, we find that the abnormal returns are large and significant after the pre-trade announcement, showing that the market takes into consideration the information content of the insiders' intention to trade. In addition, no significant stock price effect is found after the announcement of the actual trade occurrence or cancellation. We argue in favor of the implementation of advance disclosure policies since they help mitigate the returns obtained by insiders, better share the profits with outsiders and contribute to a better information dissemination.

Mediation and strategic delay in bargaining and markets

Mediator proposals can accelerate agreement and increase welfare even if the mediator is entirely uninformed. We demonstrate this by adding random mediation to the Cramton (1992) bargaining model. Mediation increases welfare by pooling types, which reduces signaling costs. Size discovery mechanisms in financial markets, in which traders can trade at a frozen price, are analogous to mediator proposals and may increase gains from trade in markets in which traders are reluctant to signal trading intentions by posting limit orders.

Trading strategy based on investment diversification technical analysis model

This paper takes gold and bitcoin as research objects and develops a model for traders. In this period, we established a multivariate quantitative technical analysis model of investment, selected 6 technical indicators with low correlation MA, MACD, BOLL, RSI, KDJ and Stoch RSI as evaluation indicators of trading intentions, and used the fuzzy evaluation model to combine the trading intentions of the 6 indicators to obtain the final daily trading intentions score. Combined with the trading intention score and risk tolerance coefficient of the day to adjust the asset allocation, the investment trading model proposed in this paper can provide support for relevant practitioners and has practical application significance.

New Classical and Bayesian Estimators for Classifying Investor Intentions from Trade Data

We propose methods for estimating the effective bid-ask spread and classifying trades without access to quotes compatible with the conceptual framework for uncovering trading intentions outlined in Easley, Lopez de Prado, and O’Hara (2016). Our state-space approach accommodates informational asymmetries and unbalanced and auto-correlated order flow. The resulting parameter estimates possess a structural model interpretation, providing alternatives to existing semi-parametric bid-ask spread estimators. An analysis of trading in CME’s gold futures contract surrounding the UK’s June 2016 Brexit referendum, indicates the spread estimates and trade classifications reflect information flow dynamics during this period of financial market uncertainty and information asymmetry.

An Analysis of the Differences in Overseas Buyers’ Perception on Korean Consumer Products

Background/Objectives: This study is about the difference of perception among overseas buyers who are interested in Korean consumer goods. The purpose of this study is to analyze whether there is a difference in price, quality, differentiation (uniqueness), reliability, and post-consultation intention for buyers to purchase representative consumer products in Korea.Methods/Statistical analysis: For the research method, we surveyed 420 overseas buyers who participated in the SME Consumer Goods Product Purchasing Meeting held in Seoul in 2019. 660 valid surveys were collected from infant, cosmetic, fashion, food and household buyers in Southeast Asia, China, Japan and other regions (Europe, USA, CIS). With SPSS22.0, we analyzed exploratory factors and reliability of Korean buyers’ characteristics and overseas buyers’ evaluation criteria for Korean companies and products. And, ANOVA was used to examine the difference in buyers’ awareness of consumer goods and product characteristic.Findings: According to the analysis results, there is a difference in recognition of price and trading intentions for the product group (baby goods, cosmetics, fashion goods, food and household goods, etc.) regardless of the buyer’s region. Therefore, price correlates with buying decisions and it can be seen that price influences trading decisions. However, there is no difference in perception in quality, differentiation and reliability, and we can see that the quality of consumer products in Korea is generally well received and high in reliability. On the other hand, buyers from different regions showed differences in perception in price, quality, and reliability, but there were no perception differences in product differentiation or intention to trade. Therefore, although prices, quality, and reliability are evaluated differently, it can be seen that there is a high willingness to trade in acknowledgment of product differentiation or uniqueness.Improvements/Applications: In the future, it is hoped that the research will be conducted only for buyers in Southeast Asian countries, and through this, it will be possible to establish marketing strategies by analyzing buyer tendency of each country in Southeast Asian countries. In addition, we hope to analyze more specific marketing points through research linking buyers’ evaluations with the performance of domestic companies.

Order Splitting and Searching for a Counterparty

Institutional investors have a strong incentive to find natural counterparties to be able to trade larger amounts at lower costs. We show theoretically that order splitting allows institutional investors to gradually detect each other's trading intentions, such that they can coordinate their trading to maximize gains from trade. Empirically, we confirm that investors detect counterparties in real-time and adjust their trading rate accordingly. The economic magnitudes are sizeable, as a one-standard deviation increase in natural counterparty trading volume correlates with a 16.9% increase in parent order size and a 91% reduction in average implementation shortfall.

Market Design for Trading with Blockchain Technology

Blockchain or, more generally, distributed ledger technology allows to create a decentralized digital ledger of transactions and to share it among a network of computers. In this paper, we argue that the implementation of this technology in financial markets offers investors new options for managing the degree of transparency of their holdings and their trading intentions. We first identify two intrinsic features of a distributed ledger that impact the availability of these new options, namely the mapping between identifiers and end-investors and the degree of transparency of the ledger, and we then examine how the implementation design of these critical features affects investor trading behavior, trading costs, and investor welfare, in a theoretical model of intermediated and peer-to-peer trading. We find that, despite the risk of front-running, the most transparent setting yields the highest investor welfare. In the absence of full transparency, for low levels of liquidity in the intermediated market, welfare is highest if investors are required to concentrate their holdings under single identifiers.

Discerning information from trade data

How best to discern trading intentions from market data? We examine the accuracy of three methods for classifying trade data: bulk volume classification (BVC), tick rule and aggregated tick rule. We develop a Bayesian model of inferring information from trade executions and show the conditions under which tick rules or bulk volume classification predominates. Empirically, we find that tick rule approaches and BVC are relatively good classifiers of the aggressor side of trading, but bulk volume classifications are better linked to proxies of information-based trading. Thus, BVC would appear to be a useful tool for discerning trading intentions from market data.

Two Shades of Opacity: Hidden Orders versus Dark Trading

Today traders are faced with an increasing number of possibilities to hide their trading intentions. In general these options can be classied into two broad categories: (1) hidden orders on otherwise lit trading venues, and (2) completely dark trading venues. Our paper investigates the relation between both types of opaque trading. Using a detailed high-frequency dataset, we rst show that hidden and dark trading are substitutes: an increase in hidden order trading implies reduced dark trading and vice versa. Second, we show that volatility and imbalanced trading drive the segmentation of trading activity into a visible and an opaque part. Third, we identify market quality variables (spread and visible depth) that drive opaque trading activity either to hidden orders on lit venues or to dark trading venues. Finally, we document a strong positive relation between the fraction of hidden order trading on several alternative lit trading venues.

Mathematical Aspects of Delayed Market Clearing in Order Driven Markets and its Applications to Non-Markovian Price Impact and Optimal Execution

The functioning of limit order books (LOBs) creates strong incentives for market participants to conceal their trading intentions. The massive submission of limit orders is inhibited, available liquidity is low even in "liquid" markets, and high-volume traders need to split orders into a long sequence of small trades. Therefore, limit order driven markets never completely clear despite a well-defined mid-price in the LOB. This paper develops a macroscopic market clearing model for the trading intentions which has the following properties: Latent orders are submitted gradually with a flow proportional to the amount of outstanding latent liquidity, the price impact of the aggregate market order flow is linear, and high-frequency fluctuations of the LOB are completely neglected. We study the mathematical properties of the resulting price impact equations and introduce a non-Markovian optimal execution problem.

Order Exposure and Liquidity Coordination: Does Hidden Liquidity Harm Price Efficiency?

We develop a model of an order-driven exchange competing for order flow with off-exchange trading mechanisms. Liquidity suppliers face a trade-off between benefits and costs of order exposure. If they display trading intentions, they attract additional trade demand. We show, in equilibrium, hiding trade intentions can induce mis-coordination between liquidity supply and demand, generate excess price fluctuations and harm price efficiency. Econometric high-frequency analysis based on unique data on hidden orders from NASDAQ reveals strong empirical support for these predictions: We find abnormal reactions in prices and order flow after periods of high excess-supply of hidden liquidity.

Private information flow and price discovery in the U.S. treasury market

Using intraday data, we identify the intensity of private information flow in the U.S. Treasury market. Our results show that the intensity of private information flow is highly correlated with public information shocks and higher for longer maturity bonds. More importantly, we find that bond price changes associated with high intensity of private information flow tend to be persistent, whereas those associated with low intensity of private information flow are more likely reversed. While public information and private information are the main determinants of bond price variations on days with news announcements, private information and liquidity shocks are important determinants of bond price variations on days with no significant events. Finally, we show that the depth of limit order book is inversely related to the intensity of private information flow. Nevertheless, informed dealers do not seem to use hidden orders to disguise their trading intentions.

Intensity of Private Information Flow and Price Discovery in the U.S. Treasury Market

Using intraday data, we identify the intensity of private information flow in the U.S. Treasury market. Our results show that the intensity of private information flow is highly correlated with public information shocks and higher for longer maturity bonds. More importantly, we find that bond price changes associated with high intensity of private information flow tend to be persistent, whereas those associated with low intensity of private information flow are more likely reversed. While public information and private information are the main determinants of bond price variations on days with news announcements, private information and liquidity shocks are important determinants of bond price variations on days with no significant events. Finally, we show that the depth of limit order book is inversely related to the intensity of private information flow. Nevertheless, informed dealers do not seem to use hidden orders to disguise their trading intentions.

Does Hidden Liquidity Harm Price Efficiency? Equilibrium Exposure under Latent Demand

We develop a model of an order-driven exchange competing for order flow with off-exchange trading mechanisms. Large investors can trade in either the primary market or the off-exchange market and induce liquidity externalities. Liquidity suppliers in the primary market face a trade-off between the costs and benefits of order exposure. If they display their trading intentions, they can benefit from elicited order flow from latent investors. In an equilibrium analysis, we show that hiding orders can cause adverse effects for the individual and the overall market. In fact, the resulting miscoordination between liquidity supply and demand causes price reactions and harms price efficiency. Using a unique dataset of hidden orders in NASDAQ trading and an econometric high-frequency analysis, we find strong empirical support for our theoretical predictions. We find abnormal reactions in prices and order flow after periods of high one-sided hidden order volume.

Online Algorithms in High-frequency Trading

HFT (high-frequency trading) has emerged as a powerful force in modern financial markets. Only 20 years ago, most of the trading volume occurred in exchanges such as the New York Stock Exchange, where humans dressed in brightly colored outfits would gesticulate and scream their trading intentions. Nowadays, trading occurs mostly in electronic servers in data centers, where computers communicate their trading intentions through network messages. This transition from physical exchanges to electronic platforms has been particularly profitable for HFT firms, which invested heavily in the infrastructure of this new environment.

Dark Trading on Public Exchanges

Over the last decade, market participants increasingly use trading tools that allow them to hide their trading intentions. We study how “dark trading” in the form of fully hidden, or dark, orders posted on a visible exchange affects the quality of the visible market. Dark orders were introduced on the Toronto Stock Exchange in 2011 in two stages, allowing us to employ a difference-in-differences approach to isolate the causal effect of the availability of dark trading. Using order level data, we observe that the introduction of dark orders led to a widening of displayed quoted spreads and an increase in trading costs, while leaving depth, overall volume, and volatility unaffected. At the intra-day level, dark trading leads to decreased displayed quoted spreads, increased depth, increased volume and to reduced trading costs and volatility. These findings may be interpreted as two sides of the same coin: the possible presence of dark liquidity causes market participants to post visible quotes more carefully. Upon detecting dark executions, however, traders infer that dark liquidity is diminished and thus may post quotes more aggressively.

Concealing the Trading Footprint: Optimal Execution Horizon

Multiple empirical studies have shown that Order Flow Imbalance has predictive power over the trading range.The PIN Theory (Easley et al. [1996]) reveals the Microstructure mechanism by which: Market Makers adjust their trading range to avoid being adversely selected by Informed Traders; Informed Traders reveal their future trading intentions when they alter the Order Flow; Consequently, Market Makers’ trading range is a function of the Order Flow imbalance.The Optimal Execution Horizon (OEH) algorithm presented here takes into account order imbalance to determine the optimal participation rate.