1. Introduction
Futures markets play a critical role in hedging trading risks, driving the price discovery, and guiding sustainable agricultural production. Transaction costs and liquidity are important indicators for measuring the efficiency and maturity of futures markets. The Bank for International Settlements (BIS) gives a comprehensive definition of market liquidity, which enables market participants to trade quickly, without causing large fluctuations in the price of financial assets. Hence, liquidity costs could be considered as the transaction costs for the sake of assuring liquidity. In practice, however, futures markets may present new challenges for market participants and regulators, since information asymmetry is prevalent and may cause an imbalance between fundamental values and market prices. If futures markets possess good liquidity, multifaceted information can be reflected promptly along with transactions characterized by large trading volume and high market participation, which would be conducive to effectively reducing risks of prices high volatility. In such an environment, investigating how intraday trading activities and daily liquidity costs affect asset prices, which attribute the effects present when trading in different intervals, will yield meaningful implications for market participants.
The topic of the liquidity of commodity markets sparked the interest of academic literature. Many researchers were committed to verifying the effectiveness of liquidity proxies when high-frequency data were not available; however, the effectiveness of those measurements remains an open question [
1,
2,
3]. Based on accurately measuring the liquidity index from high-frequency data, our objectives in this paper were to better understand the impact of intraday trades on futures price from information asymmetry perspectives, and to explore how daily liquidity affects asset pricing and its day-of-the-week effect. Obviously, the answers for the above outstanding issues contribute to providing guidance about futures trading efficiently and giving a further boost to the sustainability of the Chinese agricultural futures market.
A large body of literature, which pays more attention to the liquidity seasonality, refers to two terms: whether liquidity has any significant difference at different trading times within a day (intraday effect), and whether there is a significant difference in liquidity from Monday to Friday (weekday effect). Statistical tests were used in most relevant literature and showed that the liquidity level of an asset (expressed by the bid–ask spread) changes over time and looks generally U-shaped on a daily or weekly basis [
4,
5,
6]. Some scholars also tried exploring other empirical studies to access the characteristic of liquidity. For example, Cenesizoglu and Grass [
7] disentangled bid- and ask-side liquidity to better understand the determinants and commonalities of liquidity, and to explore whether the factors driving liquidity differ between bid- and ask-side. Obviously, exiting literature takes mainly the stock market and bond market as the research objects, and much less is known about the agricultural futures market. This paper contributes insight into the liquidity variation of agricultural commodities traded at futures markets in China. When separating day trading and night trading into individual sections, our results support previous findings about the U-shaped pattern of the intraday liquidity trend, whereby the liquidity is higher during the opening and closing periods than the middle of trading hours. Next, based on statistical analysis of the liquidity distribution, we empirically explore the intraday seasonality of informed trading in the Chinese agricultural market, taking corn and soybean as examples.
Informed trading was studied and documented early in market microstructure literature. Using the market microstructure model, Kyle [
8], Admati and Pfleiderer [
9], Foster and Vishwanathan [
10], and Chan and Fong [
11] considered uninformed traders as noise traders, and studied the trade strategies of informed traders. Mcgroarty et al. [
12] pointed out that trade volumes could respond sensitively to information flows, and both of them are closely associated with informed trader activity. Schlag and Stoll [
13], who used a basic regression approach to investigate volume–price impact, drew a conclusion that the effect of trade volume on the German DAX index is largely permanent, thus confirming the presence of information effects. Under similar model settings, Chang et al. [
14], Ryu [
15], and Webb [
16] proved again that informed trading is prevalent in most futures markets, and found that it is mainly concentrated in the opening period. When being conscious of microstructure models being sensitive to model biases and requiring unrealistic assumptions, we follow Webb [
16] and utilize regression models as alternatives for microstructure models to reduce estimation errors and noise. We use high-frequency records associated approximately with each trade from January to April in 2018 to analyze the price impact of trading volumes and attempt to figure out the presence or absence of informed trades and its seasonality. Our research supports the existing consensus that futures trades as carriers of information have significant explanatory power for the price movements of underlying assets. However, in terms of whether investors may prefer trading in a specific interval according to their trading motives and goals, the differences in the patterns of intraday trading make the findings in the soybean market not necessarily acceptable for the corn market. Simply stated from our findings, while the significant seasonality of price impact is not found in the soybean market, informed trading is concentrated in the opening and closing periods in the corn market. We think that the accessibility of night trading and the linkage extent with foreign markets are at the root of differences.
The other purpose of this paper was to investigate the heterogeneous performance of liquidity pricing and its variability in the futures market of China’s bulk agricultural products. In theory, liquidity is closely related to asset pricing; liquidity drives a wedge between the returns an investor might realize net of trading costs and the gross returns used in most asset pricing tests. Since the relationship between liquidity costs and asset pricing was first proposed and explored by Amihud and Mendelsonian [
17] from a micro perspective, it attracted wide attention in the financial world, especially in the stock market and bond market. According to their views, the return of assets is positively correlated with liquidity costs, which means that the higher liquidity costs are, the higher the return rate will be. Amihud [
18] confirmed again his conclusion of the stronger illiquidity of the stocks market resulting in higher excess returns in the same period. However, Kadlec and Mcconnell [
19] summarized that the increase in liquidity (decrease in bid–ask spread) would significantly increase the stock returns, inconsistent with the theory of Amihud. Empirically, different measurement methods or sample data may bring diverse results [
20,
21,
22,
23,
24,
25], even a few of which suspect the stable existence of the liquidity premium phenomenon, especially in some emerging markets [
26]. Some researchers [
27,
28,
29,
30] empirically revealed seasonality characteristics of liquidity pricing as well. Liu and Jiang [
31] devised an asset pricing model which also accounts for the economics of scale and the cyclical effect in the futures market, finding that liquidity cost should be included in the excess return and it has a cyclical effect on asset prices. Most empirical studies paid more attention to the generality of the liquidity effect rather than the heterogeneity among different commodities. To fill the gap, this study implements a complementary analysis of liquidity variation using high-frequency measures from January 2016 to December 2017. The results propose the view that the effects of liquidity on asset prices are statistically significant and economically important. However, they are less supportive of continuous liquidity premium effects, especially for the soybean market. That is, the influence of transaction cost on returns manifests a significant Friday effect with an apparent linear negative relationship in the soybean market. Additionally, although there appears to be a significant linear positive relationship in the corn market, the effect only occurs in Monday, since they often pay more for liquidity on Monday after acquiring private information during weekends in order to reduce delay cost and increase returns.
The rest of the paper is set out as follows:
Section 2 describes the Chinese soybean and corn futures markets in detail and clarifies the reasons for selecting these markets as ideal settings for this study.
Section 3 introduces the basic features of the dataset, including sample data and a liquidity benchmark.
Section 4 details the empirical model employed in the study, with corresponding empirical findings.
Section 5 discusses and explains the empirical results. Finally,
Section 6 concludes and presents ideas for further work.
2. The Chinese Commodity Futures Market
Futures trading in China has a short but high-growth history. Since China accelerated the transformation from a centrally planned to a market-oriented economy, Chinese commodity markets developed dramatically. Next, we successively introduce Chinese agricultural spots and futures markets, and explain which representative setting soybean and corn markets provide for our research questions. Based on our brief introduction for the running procedure of the Chinese futures market, trading types and how to impact dynamic assets price are illustrated in the final sub-section.
2.1. Chinese Agricultural Market
Dalian Commodity Exchange (DCE) became one of the most successful and significant commodity futures exchanges in China. Its soybean and corn futures prices are the most important price signal for China’s farmers, market participants, and other users. The liquidity of both markets, as measured by effective spread, is continuously climbing, driven by the facts that spots trades grow simultaneously and futures investors increase accordingly.
Since 1994, China essentially unrestricted soybean imports, and the Chinese soybean market is of particular interest to other soybean-producing countries because of the current serious import dependence of soybean products. On average, China produces 14.2 million tons of soybeans, almost all of which are used in food, and oil-pressed soybeans are imported entirely, with annual imports reaching 97 million tons. According to customs data, China imported 95.55 million tons of soybeans in 2017, of which 32.86 million tons came from the United States, 50.93 million tons from Brazil, and 6.58 million tons from Argentina. Unlike high self-sufficiency agricultural staple foods such as rice, wheat, and corn, soybean was removed from the category of “strategic” crops, and its price is prone to being influenced by the external market environment. Because of this, the soybean futures market is increasingly filled with hedgers who are interested in reducing risk and speculators who attempt to profit from price fluctuations.
Due to the food security strategy, farmers of corn were supported by the Chinese government for self-reliance. One of the major farm policies, called the temporary purchases and storage policy, was to purchase surplus grain from farmers at the targeted floor prices if the market prices fell below the floor prices, such that the price trend of corn was strongly dominated by policies. Stable market expectations and high reserve capacities of state warehouses greatly compressed investment space and effectively reduced speculation in the futures market. The government gradually canceled the temporary purchases and storage policy after 2016, and the corn market that used to be controlled by the Chinese government attracted more arbitragers; however, there are still fewer speculators entering the market due to narrow profit margins.
2.2. Chinese Commodity Futures Markets
In contrast to most quote-driven commodity futures markets, Chinese futures markets operate as a purely order-driven market, where all orders are transacted through the centralized limit order book and matched automatically by computers. To be specific, 8:55–8:59 a.m. of the trading day is the collective bidding period, and then opening prices are brokered and determined in the next minute. After that, the market enters the procedure of successive bidding, and all the incomplete price limit orders stay in the order book. If the new limit order is a purchase order, the transaction can be completed at the cost of optimal commission sale price when the entrusted purchase price is higher than the optimal commission sale price on the order book; otherwise, the order stays sequentially in the order book and waits for the new sell order that meets the transaction conditions. Similarly, if the new order is a sell order, the transaction goes with the parallel rules. Automated systems are increasingly applied to monitor the markets for trading opportunities and execute the trades as soon as trade criteria are met, since computers respond immediately to changing market conditions. In addition, China’s commodity futures market adopts a T + 0 trading mode, attracting most small and medium traders to participate in trading and holding positions. Combined with the margin system, trading boards, and daily debt-free system, they encourage investors to choose (or be forced to) leave the market to reduce losses or lock in profits after big price swings, thus working together to form a sustainable mechanism for China’s commodity futures market.
For most futures varieties, the agricultural trades in DCE start from 9:00 to 11:30 a.m., during which trades are closed for 15 min (i.e., from 10:00 to 10:15 a.m.), and from 1:30 to 3:30 p.m., almost synchronized with the underlying spots market. In addition to general trading hours, there is night trading from 9:00 to 11:30 p.m. for soybean products, nearly keeping in step with the Chicago Board of Trade (CBOT) bidding openly from 9:30 a.m. to 1:15 p.m. in the Central time zone. The trading time of Chinese futures contracts without night trading does not coincide with that of the CBOT, one of the main trading venues of international agricultural products futures. Overnight price change may occur. For example, asset price often changes from the close to the open. In particular, if great changes take place in the futures market due to an international bombshell, the domestic price cannot fluctuate in a timely manner, which will bring about price gaps at the opening of the next day. For some futures with night trading, the domestic price could be linked tightly to the external market; thus, investors adjust timely the position in light of the market quotation. That is, the greatest benefit of night trading is to reduce the risk of overnight positions, especially for the varieties which have a strong connection with international markets. Based on the discussion above, there appears great differentiation among different varieties in terms of policy environment and market mechanism. Therefore, translating the results of the soybean market to the corn market is not a straightforward exercise, and vice versa.
2.3. Informed Trading and Uninformed Trading
The movements of asset prices are mainly derived from information which is carried to markets by different investors, and asset prices appear to have various degrees of volatility in the process of responding to information. Market impact represents the movement in the asset price caused by a particular trade or order. In the financial market, price impacts can be divided into permanent impact caused by information effect, and temporal impact resulting from the depletion of the liquidity supply when executing big deals [
16]. As shown in
Figure 1, market impact occurs due to the information content (permanent) of the trade and the liquidity demand (temporary) of the trader, where the former can change the price balance and the latter does not prevent new prices from reverting in successive intervals. A permanent impact of informed trading on futures prices is irreversible, while the price change caused by a temporary impact would recover after large trading. Once the transaction is completed, the partial information of informed trading would be reflected in the prices. Following subsequent repeated exchanges, the market prices continuously reveal the information until they approach the real value. A trade price may become stable in that it no longer reflects all available information, whereas the order book may be updated at any time, even with no trading taking place.
Many investors consider transaction cost analysis as a decision-making tool and then select their trading algorithms, utilizing valuable opportunities to increase returns [
32]. Algorithmic trading remains an essential ingredient to achieve the best execution and reduce transaction costs. Investors who are properly managing all phases of the trading activity can minimize (if not avoid completely) all potential costs, except for the risk of overnight holding position. Generally, informed traders usually utilize their advantages strategically to make profits by controlling trade volumes and trade immediacy in certain periods [
10,
11]. As such, informed traders have to submit their order as soon as they can in order to compete for price/time priority during the process of private information diffusing. However, they may unintentionally convey information to the market about their trading intentions and order size (information leakage) as well. As a consequence, uninformed traders, without superior information, could only estimate the value and liquidity of futures contracts according to transaction records, and utilize the statistical edge to further determine their optimal order. Thus, they would bear a huge loss to liquidate the futures contracts when informed trades predominate, and rational investors should avoid trading in those periods of time [
33,
34].
Consequently, in order to measure the efficiency of Chinese agricultural futures markets more comprehensively and to guarantee sustainable trading for investors, more in-depth studies are needed to reach a definitive conclusion about the impacts of trading activities and market liquidity on asset prices, as well as the mechanism of these impacts.
3. Sample Data
This study examines real-time trade of Chinese soybean and corn futures from January 2016 to December 2017 on a daily basis, as well as 15-min data from January 2018 to April 2018. We get the data of individual contracts from the Dafuweng database, each of which includes the latest price, bid price, sell price, holding position, open position, trading volume, and the specific time of each transaction that is accurate within seconds. Then, we construct 15-min continuous series using order data of the most active contracts from January 2018 to April 2018, and daily continuous series using order data from January 2016 to December 2017. This paper selects the contract with the largest trading volume and the most active trading to analyze the continuous sequence. Not only could this method structuring continuous contracts get good representative indicators such as futures price, trading volume, and holding volume, but it should also overcome the discontinuity of price series and effectively avoid the price jump of the contract junction. In total, we used over 14,000,000 observations from 2016 and 2017, and 1,800,000 in 2018 (from January to April), among which about half were actually complete.
Liquidity is a very abstract concept, and we introduce its quantification below. The measurement of transaction cost we considered is spread, which is the cost of transacting at the best bid or ask quote. Based on high-frequency data, we used a well-established liquidity benchmark to measure the transaction costs: effective spread, provided by Bessembinder and Kaufman [
31]. It is measured as two times the absolute value of the natural log of the trade price minus the natural log of the bid–ask midpoint prevailing at the time of the trade.
Effective Spread = 2 × |ln (Pk) − ln (Mk)|, where Pk and Mk are the price of the k-th trade and the midpoint of the prevailing bid price (Bk) and ask price (Ak) at the time of the k-th trade, respectively. The 15-min and daily observations are correspondingly calculated as the average during intervals.
Table 1 presents the summary statistics of the sampled data of the soybean and corn futures from January to April in 2018, including the time-series mean, standard deviation, 25th percentile, 50th percentile, 75th percentile, and maximum values of the positive (buy) and negative (sell) amounts for futures trades during one-minute intervals.
Table 1 shows that the corn futures have larger trading volume than the soybean futures. Upon comprehensively considering the trading frequency during this period, 390,000 times for corn futures and 566,000 times for soybean futures, we can see that small trades are dominant in the soybean futures market. We can further infer that the soybean market is highly speculative and filled with short-term trades. For soybean futures, the day-trading volume constitutes a larger portion of total trading volume than night trading, whether negative volume (NV) or positive volume (PV). Its higher standard deviation reflects the fact that day trading is more heterogeneous and has a wider variety of trading motives than night trading. Moreover, negative futures volume is slightly higher than the positive futures volume for both categories.
One of the main research questions of this study was whether informed trading is concentrated in a specific intraday time period, particularly the opening or closing period of each trading day. To obtain a broad outline of the intraday trading pattern, we measured the trading volumes, consisting of 5-min intervals for the opening (9:00–9:15 a.m. and 9:00–9:15 p.m.) and the closing (2:45–3:00 p.m. and 11:15–11:30 p.m.) periods and 15-min intervals for other periods. Soybean futures trade in two sections per day; the opening period includes 9:00–9:15 a.m. and 9:00–9:15 p.m., while the opening period for corn futures is only 9:00–9:15 a.m. Similarly, the closing period of soybean futures includes 2:15–3:00 p.m. and 11:15–11:00 p.m., while that of corn futures is only 2:15–3:00 p.m. To ensure a fair comparison among intervals which have different time lengths, we calculated the normalized futures trading volume—the average trading volume divided by the corresponding time length.
Figure 2 presents the normalized trading volume (i.e., the trading volume divided by the length of the minute interval) in each intraday interval for soybean and corn.
As shown in
Figure 2, effective spread, for both the day-trading section and night-trading section, exhibits a nearly U-shaped pattern. That is, the liquidity cost drops significantly after the opening and then remains relatively stable until trading hours approach the closing period, during which it obviously rises. The results are consistent with previous studies of Kyle [
8], Ryu [
15], and Mcinish and Wood [
21] which agreed that severe information asymmetry should be responsible for the lack of liquidity during the phases of opening and closing. However, transaction costs of the soybean market are apparently lower than that of the corn market; thus, soybean futures with greater liquidity are more likely to attract speculators compared to corn futures, consistent with the discussion above.
Furthermore, intraday trading volumes of two varieties also follow the U-shaped variation pattern, which specifically shows that the trading volumes in the opening period are a bit higher than those in the closing period. One possible interpretation is that traders may prefer trading in opening periods to take full advantage of the information accumulating overnight, and trading in closing periods to reduce the risk of holding positions overnight. Higher liquidity may attract informed traders, and increase the proportion of informed trading during these periods. Nevertheless, night trading in the soybean market can not only balance all-day distribution of trading volumes, but also the response to information without delay and a lower share of informed trading in the opening and closing periods. Therefore, the soybean futures market may have a less significant opening or closing effect than the corn market, and the smoother slopes of trade volumes changing in the opening and closing periods can attest to that.
Figure 3 shows that corn futures have larger transaction costs than soybean futures, consistent with the findings in
Figure 2. It is well known that the smaller trade size is, the more liquidity traders there will be; thus, the expectation of larger transaction costs following larger trade sizes appears to be reasonable in the corn market. However, this is not always the case for the soybean market. When trade size is relatively small, a negative relationship between trade size and transaction cost implies an economy of scale in trading; when the trade size grows to a certain degree, the expectation of a positive relationship between trade size and transaction costs will occur, that is, a larger trade size could bring a greater price response against the backdrop of lacking liquidity.
6. Conclusions
Based on high-frequency data on real-time transaction details of soybean and corn from the Dalian Commodity Exchange (DCE), this paper empirically analyzed the liquidity of the Chinese agricultural futures market and its impact on futures price. We undertook two key steps. Firstly, we explored the price impacts of futures trades and their intraday seasonality using a basic regression framework. Secondly, we investigated how transaction costs affect asset pricing and their periodic features using an asset pricing model. We found that variances in investor components, trading patterns, and market activeness across agricultural commodities produce economically meaningful differences in liquidity and seasonality. In addition, these results highlight the importance of establishing an early warning system, improving market transparency, and perfecting the information disclosure system.
The first contribution of this research is it being able to provide traders with the necessary information to successfully utilize futures markets. It can help investors become better informed when facing public and market-wide resources, and discover trade opportunities by predicting short-term price movements. Another contribution is that the research provides a new perspective into studying the price impact of trading activities and market liquidity. Most importantly, it encourages researchers to take heterogeneity among markets into account and to focus on the different features of price impact among varieties. Although the empirical findings were based on relatively short time periods, they were accepted by the robustness tests, which are not listed in this paper to keep the paper reasonably concise. We agree that further analyses on transaction cost analysis and algorithmic trading should be carried out to provide additional insight into price impacts and the informative role of futures trading.