freecasinoslots| What are the best indicators of trading congestion in our eyes? (Tianfeng Macro Lin Yan)

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Turnover rate, as a common congestion index, has obvious defects in agency position, and there are also pain points in the referencability of transactions, so we tend to use overbought and oversold technical indicators such as deviation rate to describe transaction congestion.

ArticleFreecasinoslotsTianfeng Macro Song Xuetao / Lin Yan

In 1986, Gary Brinson mentioned in his famous paper "Determinants of Portfolio Performance" that "asset allocation can explain portfolio 93."Freecasinoslots.6% of performance fluctuations. " I think the remaining 10% may come from trading. The relationship between configuration and trading is like that between ingredients and cooking. High-end ingredients often require only the simplest way of cooking, but if everyone's "ingredients" are similar, then the importance of "cooking" will be greatly increased.

Since last year, it seems that there has been a gradual consensus on the need for barbell asset allocation in economic transformation, and the allocation may gradually come to a break. It is also time to pay attention to the selling points of the "overcrowded" sector and the buying points of the "undercrowded" sector.

At the configuration level, we pay more attention to the two dimensions of odds and odds (our methodology will be gradually exposed in a later report). At the transaction level, we pay more attention to capital flow and congestion. Today we would like to share with you our ideas for building congestion.

In investment, "crowding" means that investors hold too much of a certain type of assets. Unfortunately, the frequency of such data is not high, the timeliness is not strong and the coverage is general. For example, public funds disclose once a quarter, often up to 15 working days later than the reporting period, and the total public offering position accounts for less than 6% of the total market value of A-shares and less than 15% of the free-floating market capitalization.

Therefore, the most common way to build transaction congestion is to use trading volume (for horizontal comparability and often standardized as turnover) as a high-frequency proxy variable of position. It should be noted that there is an essential difference between the two: positions are vector, with direction, high represents overcrowding, and vice versa, while trading volume is scalar, which does not represent the direction of emotion, and high volume indicates active trading. If there are so many purchases, there are corresponding sales, which can be optimistic or pessimistic, and do not represent the direction of emotion.

Therefore, there is a logical problem in taking trading volume (or turnover rate) as the proxy variable of position.

Secondly, in terms of transaction guidance, the mean regression characteristic of turnover rate and the leadership to the inflection point are relatively weak. Taking the top three industries of public offering fund positions (2023 Annual report) as an example, comparing the indices and turnover rates of biomedicine, electronics, food and beverage (see figure 2 for details), we can clearly find the pain points in the use of these indicators:

1. High turnover rates and index highs often do not occur at the same time (the most typical example is the decline in pharmaceutical and electronic turnover rates after reaching record highs in February 2020, but the subsequent index remains strong).

2. The high point of each round of turnover rate is not consistent, so that when the turnover rate breaks through the previous round high, it is impossible to judge whether it is too crowded.

3. The hub of turnover rate rises with the increase of industry configuration weight, which further limits the reference value of historical experience to the current position.

4. The low point of turnover rate is even worse to indicate the bottom of the market, and the turnover rate is often passivated at the bottom of the price.

Turnover can be neither a perfect proxy variable of position nor a leading indicator of index inflection point, so we might as well change our way of thinking and think about how to build a reliable index of transaction congestion around availability.

To some extent, the index of overbuying and overselling is also a technical characterization of whether the market is crowded or not. The quantitative description of congestion can not avoid referring to history, and only the index with strong mean regression is suitable for comparison with history. Price deviation rate is born with this attribute, as long as the time is long enough, the short moving average always fluctuates around the long moving average.

Therefore, we are more inclined to use the price deviation rate as an indicator of transaction congestion. Comparing the relationship between the index of the triple position industry before the public offering and the historical quantile of its long-term deviation rate (half-year moving average price / annual moving average price), we can see that the mean regression attribute of long-term transaction congestion (historical quantile) is significantly better than the turnover rate; the indication of the top and bottom of long-term transaction congestion to the inflection point of the index is also significantly stronger than the turnover rate.

Of course, we can also use the quantile of the short-term deviation rate (monthly moving average price / semi-annual moving average price), which is more sensitive to short-term fluctuations, to guide shorter-cycle trading.

Currently, transactions in real estate, building materials, pharmaceuticals and consumer services are less congested, while coal, petrochemical, banking and non-ferrous metals are relatively congested.

The part with lower congestion (relatively high profit-to-loss ratio) is concentrated on the "barbell" (the part that is more related to the economy), which means that the configuration switching mentioned in "2024, investment anti-volume" (April 2, 2024) has a relatively high transaction price, gradually reaching the time when the switch can be planned. In addition, the more crowded sectors are mainly concentrated on the defensive side of the "barbell strategy". Under the assumption that the "ingredients" remain the same (the configuration maintains the "barbell strategy"), the offensive side of the "barbell strategy" (growth themes such as TMT) is more attractive to cooking (trading).

Risk hint

Policy is not as expected, economic operation is not as expected, and geopolitical risks

Team introduction

Song Xuetao | Chief researcher of Macro

Ph. D. in Economics from North Carolina State University. Published CF40 monographs, academic papers, central bank work papers and so on. 2018-2020 Taurus Award the most valuable analyst in the market, 2021 Taurus Award Best analyst, 2020-2023 Wind Gold analyst, Shanghai News Best analyst, 2019-2023 Sina Golden Kirin analyst, 2020 21st Century Gold analyst, 2020-2022 New Wealth Best analyst, 2023 New Wealth Best analyst (No. 5).

Lin Yan | researcher

Master of Financial Engineering, Wuhan University, mainly responsible for asset allocation and fundamental quantitative research.

Zhang Wei | researcher

Master of Finance, University of International Business and Economics, mainly responsible for economic policy and real estate research.

Sun Yongle | researcher

Master of Industrial Economics, Central University of Finance and Economics, mainly responsible for domestic macroeconomic and monetary liquidity research.

Zhong Tian | researcher

Master of Economics from the University of Chicago, mainly responsible for overseas economic research.

Li Mengying| researcher

Master of Regional Planning at the University of British Colombia, who is mainly responsible for macro ESG, sailing and industry trend research.