Reflective Essay
Introduction
Due to continuous improvements in information technology, electronic limit order books have become very popular as a trading channel in financial markets. No designated market maker steps in to provide liquidity in a pure order-driven market such as the Stock Exchange of Hong Kong (SEHK) (Ahn et al. 2001). The participants are individuals trading with limit orders or market orders. Market orders are filled at the best price determined by a previously submitted limit order, and limit orders specify a specific price that participants are willing to pay or accept and fill when that condition is met. The electronic limit order market is characterized by continuous trading, order book visualization, and sequential priority rules. In this market microstructure, traders submitting limit orders provide liquidity, which is in turn consumed by traders placing market orders (Bloomfield et al. 2005). In this paper, I use TRETS for order-driven market simulation trading. Firstly, I trade with market and limit orders separately and analyze the characteristics of both orders. Secondly, trading is performed using large market orders. Finally, I trade as a proprietary trader using both market and limit orders to analyze my trading behavior and trading results.
Trading simulation and analysis
First, I traded with market orders and limit orders separately. I plan to purchase 30 shares. VWAP is used as a benchmark to measure the performance of buy order execution, which is the volume-weighted average price. Traders usually expect the average purchase price to be lower than VWAP and the average sell price to be higher than VWAP during trade execution (Madhavan, 2002). Table 1 below shows the trading results with market orders. Although the task of buying 30 shares of stock was completed before the stock market closed, the results show that the loss of the trade is 1.85 and the average buy price is slightly higher than the VWAP.
Table 1 Trading Results with Market Orders
Time position price cost Profits/loss Average buy price VWAP
00:10.515 10 6.21 0.62 -1.85 6.219 6.2092
00:37.734 7 6.20 0.43 00:57.062 4 6.19 0.25 02:19.484 7 6.20 0.43 03:06.187 1 6.24 0.06 03:50.437 1 6.25 0.06 Table 2 and 3 below show the results of two trades with limit orders respectively. The average buy price of both simulated trades is lower than the VWAP.
Table 2 the First Trading Results with Limit Orders
Time position price cost Profits/loss Average buy price VWAP
02:00.063 1 5.78 0.06 -0.92 5.77 5.7975
02:10.063 7 5.78 0.40 03:20.031 5 5.76 0.29 04:30.109 3 5.76 0.17 Table 3 the Second Trading Results with Limit Orders
Time position price cost Profits/loss Average buy price VWAP
00:30.047 5 5.74 0.29 0.47 5.743 5.8000
01:30.219 5 5.74 0.29 01:40.047 3 5.74 0.17 02:10.062 2 5.74 0.11 04:11.359 1 5.79 0.06 The above results show that market orders can be executed immediately (Bae et al., 2003). However, limit orders do not have this feature. For example, I placed a limit order at 00:19.42, and this order was executed after two minutes. Second, the execution bid price of market order is higher than the limit order (Foucault et al., 2005). In the simulation, market orders are executed at 6.21, which is the best ask price, higher than the execution price of limit order (6.19). Third, limit orders carry both the risk of not being executed and of trading with better-informed traders (Handa and Schwartz, 1996). In my first trade with a limit order, I initially placed a limit order at 5.78.
However, after the limit order was executed, the price continued to fall, and the best bid and ask prices became 5.75 and 5.77, respectively. This indicates that the stock was already worth less than 5.78 which implies the risk of trading with better-informed traders. In my second demo trade, I placed a limit order at 5.74. However, the best buy price then rose to 5.78 and it did not fall back. My limit order at 5.74 was never executed. Fourth, although not all 30 shares were bought with limit orders, the limit orders performed better than market orders in terms of execution. This is also proved in the study of Harris and Hasbrouck (1996). Also, the results suggest that limit order traders benefit through mean reversion (Biais et al., 1995). In the second simulation, my limit order at 5.74 was executed and the price continued to fall, but then the price returned to its previous value and continued to rise.
Next, I traded with large market orders. I placed a 30-share market buy order which has a more significant effect on the price (Kyle’s, 1985). After a large market order is placed, the limit orders on the order book at 5.64, 5.65 and 5.66 are executed and the order book thinned. In the following period, an increasing number of sell limit orders are placed in the order book, while the price decreases and does not return to the initial level eventually. The average buy price for this trade was 5.649, slightly lower than 5.6589, but the final profit was -8.25. This indicates a greater loss than the trade that the 30 shares had been spread out.
Finally, I traded with both market and limit orders, and close positions before market close. The choice of market and limit orders is based on a trade-off between order price, execution probability, and selection risk (Harris, 1998). First, the size of the spread affects my trading choice. When the spread is small, I prefer to place a market order which can deplete liquidity and cause spreads to widen. I place a limit order when the spread was larger. For example, I place a buy limit order at 5.98 and a sell limit order at 6.02, if they are eventually executed, I get a profit of 0.04 per share. Second, my assessment of the execution probability also affects my trading. For example, when I found that many orders already existed at the best offer on the buy side of the order book, I believe that the probability of executing a limit order at that offer is low. Therefore, I prefer to place a market order or a limit order with a higher bid price. Finally, the remaining trading time affects my trading. I am more concerned about order execution than price. To close my position before the market closes, I usually ignore the price and choose a market order to trade all my remaining shares.
When considering trading performance, smaller spreads do not fully compensate for transaction costs. According to Table 4 below, although my buy and sell limit order were executed at 02:10.075, the round-trip profit of 0.02 per share could not compensate for the transaction cost of 0.11 per share, and the final profit was still negative.
Table 4 the Trading Results with Limit Orders and Market Orders
Open time Open price Close time Close price position cost profit
01:00.075 5.43 02:10.075 5.45 1 0.11 -0.09
01:20.095 5.43 02:10.075 5.45 7 0.76 -0.62
01:20.095 5.43 03:00.065 5.45 2 0.22 -0.18
01:29.465 5.43 04:24.594 5.45 1 0.11 -0.09
02:33.435 5.43 04:24.594 5.45 5 0.54 -0.39
Conclusion
Simulated trading with TRETS clarifies the trading strategies and related trading issues in order-driven markets. In this paper, I first made comparative analysis of trading performance of market and limit orders. It is important to note that although using limit orders did not achieve the target number of trades during the trading day, trading with limit orders suggests a better performance. I am usually an impatient trader in the trading process, and I often place aggressive orders to increase the probability of execution to reach my target trade size. There are certain limitations in this paper: First, it does not consider order cancellation, which has a significant impact on order trading (Peterson and Sirri, 2002). Second, due to the short simulation trading time, the number of stocks traded during the simulation trading day is not large. Also, the effect of order submission time on trading (Easley and O’Hara, 1992) was not analyzed. Finally, this paper does not analyze in detail the impact of short-term market volatility on order trading (Handa and Schwartz, 1996).
Critical Review on Liquidity Risk
Introduction
Liquidity is an important characteristic of financial markets and is often considered to have no clear or universally accepted definition. One of the widely accepted definitions is the ability to trade a large number of transactions rapidly at low cost with little impact on the price (Liu 2006). In financial markets, liquidity varies over time, which indicates that liquidity is risky, and Morris and Shin (2004) point out that the variability and uncertainty of liquidity are major challenges for financial liquidity users such as traders and investors. Liquidity indicators tend to decline extremely during market downturns (Chordia et al. 2001), such as the stock market crashes of 1987 and 1989, the Asian financial crisis of 1997 and the LTCM crisis of 1998, which are considered to be systemic collapses of liquidity. In the research field, scholars have paid extensive attention to the relationship between liquidity and asset returns. To further enhance the understanding of the current state of liquidity risk research, this paper provides a systematic review of the studies on liquidity risk. This study first reviews the definitions of stock market liquidity and liquidity risk, and then this paper compares the literature on the level of liquidity and the relationship between liquidity risk and stock returns, respectively.
Definition
From a market perspective, liquidity is the presence of buy and sell prices always for investors who want to trade a small number of stocks immediately (Black, 1971). Kyle (1985) proposed three liquidity dimensions. Subsequently, Harris (1990) further enriched the liquidity measure dimensions by proposing the four dimensions of liquidity, namely immediacy, breadth, depth, and elasticity. The proposal of the four dimensions of liquidity has been widely accepted by the academic community.
Since liquidity has multiple dimensions, it is difficult to be measured by a single indicator. Previous studies have used multiple liquidity measures which include high frequency proxies such as quoted bid-ask spread, effective bid-ask spread, quote size, trading volume and trading frequency (Glosten and Harris, 1988; Brennan and Subrahmanyam, 1996; Chordia et al; Huberman and Halka, 2001, etc.) and low-frequency proxies such as Amihud illiquidity indicator (ILLIQ indicator) (Amihud, 2002).The liquidity of an asset is subject to constant changes in response to market environmental conditions. The risk arising from fluctuations in the liquidity of an asset or the risk arising from illiquidity or illiquidity of an asset is considered to be liquidity risk. And it is also defined as the risk arising from the cost of liquidation of assets in the process of liquidation.
Liquidity Risk and Returns
Early research focused on the impact of liquidity levels on stock returns, and most showed that liquidity impacts the stock returns significantly. The first to study this kind of relationship was Amihud and Mendelson (1986). They used bid-ask spreads to measure illiquidity. Their findings suggest that liquidity has a significant impact on stock returns, with higher spread assets generating higher expected returns. Since then, most studies have also found similar findings (such as Brennan & Subrahmanyam, 1996; Lam and Tam, 2011; Dinh, 2017). Their findings point to an impact of liquidity on stock returns.
However, some literature finds opposite findings. Eleswarapu and Reinganum (1993) extend the study of Amihud and Mendelson (1986) and show that the effect of liquidity on stock returns is significant only in January. Lischewski and Voronkova (2012) choose the Polish stock market as an emerging market for their study and there is no significant evidence that illiquidity affects expected stock returns. They suggest that this may be related to the specific structure of the Polish stock market. Evidence for the absence of a liquidity premium for stocks was likewise found in a study of frontier markets (Stereńcza et al., 2020). Therefore, it is important to consider alternative views.
In the study of liquidity premiums, the focus has gradually shifted from the liquidity of individual assets to the commonality of liquidity. Chordia et al. (2000) show that liquidity indicators co-vary with market and industry-wide liquidity. This co-influence remains important even after accounting for individual stock liquidity determinants. Since then, studies such as Hasbrouck and Seppi (2001), Brockman et al. (2009) and Chuliá et al. (2020) also support liquidity commonality. The fact that liquidity commonality is time-varying makes it difficult to disperse liquidity risk, which means that this common liquidity risk may become a priced risk factor.
Pastor and Stambaugh (2003) investigate the relationship between stock returns and asset prices. They construct a single variable that measures market illiquidity and find a significant relationship between sensitivity to liquidity fluctuations and expected stock returns. Sadka (2006) finds similar evidence through their study. Subsequently, Acharya and Pedersen (2005) constructed the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) which is proved to explain the data better than the traditional CAPM model. However, there is the same pricing of market risk and liquidity risk in the LCAPM model. Liu (2006) extends the traditional CAPM model to construct a two-factor model that includes both market and liquidity factors.
Subsequently, several studies using LCAPM model (Acharya and Pedersen, 2005) have found supporting evidence (Lee, 2011; Kim and Lee, 2014; Grillini et al. 2019). However, studies of the pricing power of other liquidity risk-based models have been found differently. The liquidity factor is not priced in the U.S. equity market when the PS liquidity factor is incorporated into the asset pricing model (Momani, 2018). Further, there is also no evidence that Sadka’s (2006) liquidity factors based on price effects generate significant liquidity risk premiums. But this does not mean that liquidity risk can be ignored in asset pricing models, and Ma et al. (2021) found that LCAPM models perform well in explaining average asset returns.
In recent years an increasing number of studies have focused on emerging markets, but the related research literature has found different results. A part of the research finds that liquidity cointegration is priced in emerging markets (Lee, 2011; Ho and Chang, 2015; Silva Júnior and Machado, 2020). However, Moshirian, 2017) assert that while liquidity commonality is priced in developed markets, such results are not found in emerging markets.
Conclusion
Liquidity is vital to traders and the financial markets. This study may help researchers comprehend the present level of research on liquidity risk, notably the link between liquidity risk and stock returns. First, the paper defines stock market liquidity and liquidity risk. Liquidity is the capacity to trade huge volumes of transactions swiftly and cheaply. Second, this research examines the literature on liquidity and liquidity risk. Currently, most research shows that liquidity and liquidity risk effect stock returns. Several studies build asset pricing models based on liquidity risk to examine its pricing power. Several research have shown it to be a major price component. However, several disagreements in this subject field need additional investigation. First, liquidity risk-based asset pricing models are continually evolving. More research is required to construct complete asset pricing models that consider liquidity risk.
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