Quantitative Models and Construction Methods 1. Model Name: High-frequency "Gold" Portfolio CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This strategy combines three categories of high-frequency factors (price range, price-volume divergence, regret avoidance) equally weighted to enhance the CSI 1000 Index. It aims to leverage high-frequency data to capture microstructure insights and improve stock selection performance[4][38][39] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, regret avoidance) with equal weights (25%, 25%, 50%) 2. Apply industry and market capitalization neutralization to the combined factor 3. Implement weekly rebalancing with a transaction cost rate of 0.2% per side 4. Introduce turnover buffering mechanisms to reduce transaction costs[14][38][39] - **Model Evaluation**: The strategy demonstrates strong out-of-sample performance with stable excess returns, though it has experienced some recent adjustments[42] 2. Model Name: High-frequency & Fundamental Resonance Portfolio CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This strategy integrates high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to improve multi-factor portfolio performance. The low correlation between high-frequency and traditional fundamental factors enhances diversification[43][45] - **Model Construction Process**: 1. Combine the three high-frequency factors and three fundamental factors equally weighted 2. Apply industry and market capitalization neutralization to the combined factor 3. Implement weekly rebalancing with a transaction cost rate of 0.2% per side 4. Introduce turnover buffering mechanisms to reduce transaction costs[43][45] - **Model Evaluation**: The strategy shows improved performance metrics compared to the high-frequency-only strategy, with higher annualized returns and lower maximum drawdowns[45][47] --- Model Backtesting Results 1. High-frequency "Gold" Portfolio CSI 1000 Index Enhanced Strategy - Annualized Return: 10.56% - Annualized Volatility: 23.75% - Sharpe Ratio: 0.44 - Maximum Drawdown: 47.77% - Annualized Excess Return: 9.58% - Tracking Error: 4.36% - Information Ratio (IR): 2.20 - Maximum Excess Drawdown: 6.53%[39] 2. High-frequency & Fundamental Resonance Portfolio CSI 1000 Index Enhanced Strategy - Annualized Return: 14.80% - Annualized Volatility: 23.39% - Sharpe Ratio: 0.63 - Maximum Drawdown: 39.60% - Annualized Excess Return: 13.70% - Tracking Error: 4.23% - Information Ratio (IR): 3.24 - Maximum Excess Drawdown: 4.97%[45] --- Quantitative Factors and Construction Methods 1. Factor Name: Price Range Factor - **Factor Construction Idea**: Measures the activity of stock transactions in different intraday price ranges, reflecting investor expectations for future stock trends[3] - **Factor Construction Process**: 1. Use 3-second snapshot data to calculate transaction volume and count in high (80%) and low (10%) price ranges 2. Construct sub-factors: - High price range transaction volume factor (VH80TAW) - High price range transaction count factor (MIH80TAW) - Low price range average transaction volume factor (VPML10TAW) 3. Combine sub-factors with weights of 25%, 25%, and 50%, respectively 4. Apply industry and market capitalization neutralization[11][14][16] - **Factor Evaluation**: Demonstrates strong predictive power and stable performance out-of-sample[3][16] 2. Factor Name: Price-Volume Divergence Factor - **Factor Construction Idea**: Measures the correlation between stock price and trading volume. Lower correlation indicates higher potential for future price increases[3][19] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate correlations: - Price and transaction count correlation (CorrPM) - Price and transaction volume correlation (CorrPV) 2. Combine sub-factors equally weighted 3. Apply industry and market capitalization neutralization[19][22][23] - **Factor Evaluation**: Performance has declined since 2020 due to widespread adoption but remains stable with positive excess returns in 2023[23] 3. Factor Name: Regret Avoidance Factor - **Factor Construction Idea**: Based on behavioral finance, this factor captures investor regret avoidance emotions, such as the impact of selling stocks that later rebound[3][24] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify active buy/sell directions 2. Construct sub-factors: - Sell rebound proportion factor (LCVOLESW) - Sell rebound deviation factor (LCPESW) 3. Combine sub-factors equally weighted 4. Apply industry and market capitalization neutralization[24][28][30] - **Factor Evaluation**: Exhibits stable out-of-sample performance, indicating significant influence of regret avoidance on stock returns[31] 4. Factor Name: Slope Convexity Factor - **Factor Construction Idea**: Captures the impact of order book slope and convexity on expected returns, reflecting investor patience and supply-demand elasticity[3][32] - **Factor Construction Process**: 1. Calculate order book slope using cumulative order volume and price at different levels 2. Construct sub-factors: - Low-level slope factor (Slope_abl) - High-level convexity factor (Slope_alh) 3. Combine sub-factors equally weighted 4. Apply industry and market capitalization neutralization[32][35][37] - **Factor Evaluation**: Performance has been stable since 2016, though recent results are relatively flat[35] --- Factor Backtesting Results 1. Price Range Factor - Annualized Excess Return: 3.24% (VH80TAW), 4.45% (MIH80TAW), -0.77% (VPML10TAW)[12][14][16] 2. Price-Volume Divergence Factor - Annualized Excess Return: 2.56% (CorrPM), 2.61% (CorrPV)[19][22][23] 3. Regret Avoidance Factor - Annualized Excess Return: -2.67% (LCVOLESW), 0.33% (LCPESW)[24][26][31] 4. Slope Convexity Factor - Annualized Excess Return: -2.35% (Slope_abl), 0.02% (Slope_alh)[34][35][37]

Group 1 - The core viewpoint of the report suggests that the dividend strategy may struggle to achieve excess returns in 2026 due to a shift in market focus from dividend yield to growth rates, particularly influenced by the AI industry and improving corporate earnings in China [2][11][33] - The report indicates that the dividend strategy underperformed the market significantly in 2025, primarily due to the emergence of new growth sectors like AI, a decline in traditional manufacturing reliance, and a rise in market risk appetite [11][33] - It is noted that while the dividend strategy may not yield excess returns, it remains a crucial component for many investors as a stabilizing element in their portfolios, especially during market volatility [2][33] Group 2 - The analysis reveals that the Hong Kong stock market's low-volatility dividend index significantly outperformed the A-share market's equivalent, with a relative return of 49% attributed mainly to the industrial, financial, and energy sectors [3][36] - Despite the higher dividend yield in Hong Kong stocks, the report highlights that the absolute PE valuation levels between Hong Kong and A-share dividend stocks are now comparable, indicating limited room for further convergence [3][37] - The report emphasizes that the performance of the Hong Kong dividend stocks is primarily driven by individual stock selection rather than sector allocation, with financials, energy, and industrials contributing the most to relative performance [3][44] Group 3 - The report outlines three key themes for constructing and optimizing the dividend portfolio for 2026: the impact of overseas AI investments, resource protectionism in emerging markets, and the recovery of domestic consumption [3][22] - It suggests that resource and traditional manufacturing dividends will benefit the most from these themes, while financial dividends may only recover post-deflation [3][22] - A scoring system based on yield and profitability is proposed to identify sectors with the best potential for dividend growth, categorizing industries into four quadrants based on their risk and return profiles [3][24]

Industry Trends - The EU's revised cybersecurity law is expected to maintain a supply-demand imbalance for storage chips until 2028, with prices projected to rise by approximately 20% in Q2 2026[12] - OpenAI's annual revenue for 2025 has surpassed $20 billion, a significant increase from $6 billion in 2024, driven by expanded computing capabilities[15] - The demand for storage chips is projected to reach $551.6 billion in 2026, a year-on-year growth of 134%[13] Capital Movements - Intel's Q4 2025 revenue reached $13.7 billion, with a non-GAAP gross margin of 37.9%, but Q1 2026 revenue guidance is between $11.7 billion and $12.7 billion due to supply constraints[24] - Alibaba is considering spinning off its chip design subsidiary, T-Head, into an independent company, which could enhance its competitiveness in the AI sector[29] - Anthropic's annual revenue has increased from approximately $4 billion in mid-2025 to over $9 billion by the end of 2025, indicating strong growth in the AI model sector[16] Application Developments - OpenAI plans to test advertisements in the free and "Go" versions of ChatGPT in the coming weeks, with a new AI device set to launch in the second half of 2026[18][19] - Apple's new AI chatbot, codenamed Campos, is expected to be unveiled in June 2026, enhancing user interaction capabilities[33] - ByteDance's AI Agent platform has announced a significant upgrade, integrating various AI capabilities to better meet diverse task requirements[20]

- The report highlights the performance of eight major stock selection factors across different stock pools (All A-shares, CSI 300, CSI 500, and CSI 1000). Among these, the value factor (17.88%) and size factor (11.88%) showed strong IC mean performance, while reversal and quality factors performed relatively weaker[54][55][66] - Small-cap value style dominated the market, with the small-cap size factor performing strongly across the entire market. Value factors also showed positive performance, indicating a market preference for low valuation stocks. Additionally, technical and low-volatility factors performed well, while consensus expectation factors weakened due to reduced focus on high-performance expectation sectors[54][55][66] - The report provides detailed definitions for various factors, such as size (logarithm of market capitalization), value (e.g., book-to-price ratio, earnings-to-price ratio), growth (e.g., net income growth), quality (e.g., ROE, gross margin), consensus expectation (e.g., changes in expected EPS), technical (e.g., volume skewness), volatility (e.g., 60-day return standard deviation), and reversal (e.g., 20-day return)[66][67] - The report also tracks the performance of convertible bond selection factors, which are constructed based on the relationship between convertible bonds and their underlying stocks. Factors include stock consensus expectation, stock value, and convertible bond valuation (e.g., parity premium rate). Among these, stock consensus expectation and stock value factors achieved higher IC mean values in the past week[59][60][62]

Macro Liquidity - The US dollar index has declined, and the degree of the China-US interest rate "inversion" has deepened. The nominal and real yields of 10Y US Treasuries remained unchanged and rebounded, respectively, with inflation expectations slightly decreasing [1][15] - Offshore US dollar liquidity is generally loose, while the domestic interbank funding situation is balanced but tight. The term spread (10Y-1Y) has narrowed [1][15] Market Trading Activity - Market trading activity has significantly decreased, with the volatility of the Shanghai Composite 50 and CSI 500 indices rising. Sectors such as military, electric new energy, consumer services, chemicals, and home appliances have trading heat levels above the 90th percentile [2][25] - The volatility of the Shanghai Composite 50 and CSI 500 indices has increased, while the volatility of various sectors remains below the 80th historical percentile [2][31] - Market liquidity indicators have declined, with liquidity metrics for various sectors remaining below the 60th historical percentile [2][35] Institutional Research - The research heat for sectors such as electronics, electric new energy, automotive, computers, and machinery is high, while only the banking sector has seen a sequential increase in research heat [3][42] Analyst Forecasts - Analysts have raised net profit forecasts for the entire A-share market for 2026/2027. The proportion of stocks with upward revisions in net profit forecasts has continued to rise [4][50] - Specific sectors such as agriculture, non-ferrous metals, consumer services, computers, and electronics have seen upward adjustments in their 2026/2027 net profit forecasts [4][50] - The net profit forecasts for the ChiNext Index, CSI 500, and Shanghai Composite 50 for 2026/2027 have been increased, while the forecasts for the CSI 300 have been adjusted downwards/upwards [4][51] Northbound Trading Activity - Northbound trading activity has decreased, but there continues to be a net purchase of A-shares. The ratio of total buying and selling in sectors like electronics, automotive, and home appliances has increased [5][31] - For stocks with northbound holdings of less than 30 million shares, the main net purchases have been in electronics, electric new energy, and chemicals, while net sales have occurred in computers, media, and military sectors [5][33] Margin Financing Activity - The activity of margin financing has continued to decline, reaching its lowest level since late July 2025. The net selling last week was 8.265 billion yuan, with significant net purchases in non-ferrous metals, finance, and food and beverage sectors [6][35] - The proportion of financing purchases across various sectors has decreased [6][38] - Margin financing continues to net buy large-cap growth/value sectors [6][39] Dragon and Tiger List Trading Activity - The trading activity on the Dragon and Tiger list has continued to rise, although the total trading amount on the list as a percentage of total A-share trading has decreased [7][41] Active Equity Fund Positions - The positions of actively managed equity funds have continued to decline, while ETFs have seen significant net redemptions. Active equity funds have mainly increased positions in light industry, banking, and pharmaceuticals, while reducing positions in electric new energy, communications, and chemicals [8][45] - The correlation of active equity funds with large/mid-cap growth/value has increased, while the correlation with small-cap growth/value has decreased [8][48] - The scale of newly established equity funds has continued to rise, with active and passive funds seeing respective increases and slight decreases [8][50]

2026 年 01 月 26 日 "固收+"大发展，转债仓位继续被"稀释" 25Q4 基金转债持仓分析 固定收益专题报告 证券研究报告 固定收益组 分析师：尹睿哲（执业 S1130525030009） yinruizhe＠gjzq.com.cn 分析师：李玲（执业 S1130525030012） liling3＠gjzq.com.cn 核心观点 25 年四季度权益转入高位震荡、纯债也进入波动但利率水平较低，固收类资金继续向权益市场要收益，但转债不断新 高的估值也带动各产品规模&仓位&持券偏好出现分化。 二级债基"一只独秀"，转债仓位表现分化。1）四季度由于权益转入震荡混合型基金规模基本持平，但对转债的仓位 继续下降、不过降幅有所收窄，目前已来到 17 年，其中偏债混产品的转债仓位主动降低至 3.89%、回到 21 年初的位 置，灵活配置型的转债仓位在连续 2 年的下降后终于止跌回升到 0.66%、但仍然处于历史较低位置。2）一级债基由于 弹性有限、继续表现为小幅赎回，其中仅有转债仓位超 50%的产品表现为净申购，而利率低位的背景下仍然需要向转 债要收益，因此在经过一段时间的仓位降低后一级债基对转债仓位止跌回升 ...

Report Title - "Counting the Public Offering Pure Bond and Hybrid Asset Strategy Funds' 2025 Q4 Reports - The Scale of 'Fixed Income +' Reaches a New High, and the Exposure to Equity Slightly Declines" [1] Report Date - January 26, 2026 [2] Market Overview General Fixed - Income Fund Scale in 2025 Q4 - Among the top 20 fund companies in terms of general fixed - income fund scale, the scale of some companies increased while others decreased. For example, the scale of China Merchants Fund increased by 9.88% to 3512.27 billion yuan, while the scale of E Fund decreased by 4.71% to 3627.05 billion yuan [8]. Hybrid Asset Strategy Fund Scale in 2025 Q4 - In the hybrid asset strategy fund scale ranking, the scale changes also varied. For instance, the scale of Invesco Great Wall Fund increased by 32.11% to 2263.68 billion yuan, while the scale of Fullgoal Fund decreased by 5.85% to 1281.73 billion yuan [8]. Performance Return - Different types of funds had different average returns in 2025 Q4, year - to - date, and in the past 1 - year, 3 - year, and 5 - year annualized periods. For example, the average return of convertible bond funds in 2025 Q4 was 0.84%, and the year - to - date return was 23.10% [15]. Maximum Drawdown - The average maximum drawdowns of various fund types also differed. For example, the average maximum drawdown of convertible bond funds in 2025 Q4 was - 5.26%, and the year - to - date maximum drawdown was - 8.90% [15]. Annualized Sharpe Ratio - The annualized Sharpe ratios of different fund types were distinct. For example, the annualized Sharpe ratio of short - term pure bond funds in 2025 Q4 was 4.28 [15]. Asset Allocation Leverage Ratio - In 2025 Q4, different types of funds had different leverage ratios and their changes compared to Q3. For example, the leverage ratio of short - term pure bond funds was 111.89% in Q4, an increase of 0.40% compared to Q3 [40]. Holding Characteristics Stock Holdings - From 2025 Q1 to Q4, the industry and stock holding ratios of funds changed. For example, the proportion of non - ferrous metals in the stock market value increased from 11.27% in Q1 to 14.65% in Q4 [54][57]. Bond Holdings - The industry and bond holding ratios of funds also changed over the four quarters of 2025. For example, the proportion of bank bonds in the bond market value decreased from 20.75% in Q1 to 14.45% in Q4 [67][68]. Fund Managers' Views Pure Bond Market Views - Different fund managers had different views on the pure bond market in 2026 Q1. For example, Huang Yingjie of Bank of Communications Yulong Pure Bond A believed that the bond market might be in a range - bound market with a steeper curve [74]. Bond and Stock Market Views - Some fund managers had comprehensive views on the bond and stock markets. For example, Deng Xinyu and Zhao Yucheng of China Europe Dingli A were optimistic about the stock market's structural opportunities and adjusted their convertible bond positions [75]. Convertible Bond and Stock Market Views - Fund managers also had different views on the convertible bond and stock markets. For example, Huang Bo of Everbright Tianyi A planned to select high - cost - effective convertible bonds for the fund's fixed - income part [79].

1. Report Industry Investment Rating - No information provided in the given content 2. Core View of the Report - Last week (1/19 - 1/23), bond - type ETFs had a net capital outflow of 15.6 billion yuan. Credit - bond ETFs, interest - rate bond ETFs, and convertible - bond ETFs had net outflows of 11.7 billion yuan, 6.3 billion yuan, and a net inflow of 2.3 billion yuan respectively. Their cumulative unit net value weekly growth rates were +0.11%, +0.26%, and +2.65% compared to the previous week [2][13]. 3. Summary According to the Directory 3.1 Issuance Progress Tracking - There were no newly issued bond ETFs last week [3][17]. 3.2 Stock Product Tracking - As of January 23, 2026, the circulating market values of interest - rate bond ETFs, credit - bond ETFs, and convertible - bond ETFs were 131.1 billion yuan, 382.9 billion yuan, and 73.4 billion yuan respectively, with credit - bond ETFs accounting for 63% of the total. Compared to the previous week, their circulating market values decreased by 5.8 billion yuan, 9.1 billion yuan, and increased by 4.3 billion yuan respectively. The circulating market values of benchmark - market - making credit - bond ETFs and science - innovation bond ETFs were 109.2 billion yuan and 295 billion yuan respectively, decreasing by 5 billion yuan and 7.4 billion yuan compared to the previous week [4][19][20]. 3.3 ETF Performance Tracking - The cumulative unit net values of interest - rate bond ETFs and credit - bond ETFs were 1.19 and 1.03 respectively. The return rate of benchmark - market - making credit - bond ETFs since their establishment has continuously risen to around 1.4%, and that of science - innovation bond ETFs has risen to 0.4% [5][24][30]. 3.4 Premium/Discount Rate Tracking - Last week, the average premium/discount rates of credit - bond ETFs, interest - rate bond ETFs, and convertible - bond ETFs were - 0.17%, - 0.04%, and +0.03% respectively. The average trading price of credit - bond ETFs was lower than the fund's unit net value, indicating low allocation sentiment. Specifically, the weekly average premium/discount rates of benchmark - market - making credit - bond ETFs and science - innovation bond ETFs were - 0.24% and - 0.17% respectively [6][35]. 3.5 Turnover Rate Tracking - Last week, the turnover rate was in the order of interest - rate bond ETFs > credit - bond ETFs > convertible - bond ETFs. The weekly turnover rates of interest - rate bond ETFs and credit - bond ETFs improved, reaching 171% and 136% respectively, while that of convertible - bond ETFs decreased to 109%. Products with high turnover rates included Huaxia Shanghai Stock Exchange Benchmark - Market - Making Treasury Bond ETF, Science - Innovation Bond ETF Yongying, and Morgan Shanghai Stock Exchange AAA Science - and - Technology Innovation Corporate Bond ETF [7][40].

Report Industry Investment Rating - Not provided in the content Core View of the Report - Trump's policies in the new year aim to address domestic and international issues, providing a more favorable macro - environment for the AI narrative. The role of monetary policy is narrowing, and fiscal policy is expanding. The traditional economic policy framework is being replaced by the White House's executive power. In 2026, Trump will maximize his executive power, and the success of domestic policies will be judged by voters, while the international affairs will affect the US dollar credit [2][4]. Summary by Relevant Catalogs First Arrow: Improving Affordability Domestically - Trump uses administrative means to control living costs instead of relying on the Fed's monetary policy, aiming to stimulate the "cold" end of the K - shaped economy (low - income groups and suppressed employment) [5]. - The labor income share of the US "working class" dropped to 53.8% in Q3 2025, continuing the downward trend since 2000. Tax cuts or direct cash - handouts will increase the government transfer payment ratio and cause greater fiscal deficit pressure [6]. - Trump's direct policies include setting a 10% credit - card interest - rate cap and intervening in the housing market (launching 50 - year mortgages and having "Fannie & Freddie" buy $200 billion of MBS). The 10% credit - card interest - rate cap is controversial and likely to backfire, causing a decline in credit supply and potential moral hazards, as well as increased inflation pressure. The purchase of MBS by "Fannie & Freddie" can increase mortgage demand and compress mortgage spreads to some extent [10][14]. - Trump's administrative means rely on the Fed's support, but his attempt to force Powell to resign may backfire. His control over the new Fed chair candidate is increasing, which is more "friendly" to the capital market [16]. Second Arrow: Seeking the "Greatest Common Divisor" of US Interests Abroad - Trump's actions in Venezuela and his interest in Greenland are to seek the greatest common divisor of "US national interests, voter concerns, and his political demands". The "Absolute Determination Operation" in Venezuela aims to build a US - led "Western Hemisphere energy fortress", and his interest in Greenland is for personal political gain and to achieve national strategic goals [19]. - Trump advocates an economic nationalism model to replace the Davos globalist model. His negotiation art often involves extreme pressure, and he may use various means such as tariffs and military intervention. Assets like gold and Bitcoin will face more frequent event - driven shocks [20]. - As the marginal utility of Trump's threats decreases, he may issue secondary threats, which may lead to the selling of US assets, rising long - term US Treasury yields, and increased liquidity pressure on the US stock market [21]. Third Arrow: Maintaining AI Leadership - Trump requires AI companies to prioritize US national interests, and his domestic and international policies are to create a better macro - environment for AI development. The investment proportion of computer and related equipment and data centers is increasing [22][23]. - In 2026, the importance of external financing for AI companies has increased, and the risk of private - credit funds investing in AI is also gathering. The current stock - price increase of AI companies far exceeds the debt - market pricing, and there is a potential risk of a significant stock - price correction [27][30]. Finally: The High Cost Borne by the US Dollar Credit - Trump's policies aim to maintain the stability of the US economic system, but their dynamic impacts are complex and uncertain, including fiscal, inflation, and deficit pressures. These policies are similar to the "Modern Monetary Theory" (MMT) previously advocated by the far - left [31]. - Administrative logic can temporarily overcome economic logic, but economic laws cannot be cancelled. The costs suppressed by administrative orders may turn into future inflation, default risks, and higher systemic volatility. The cost of Trump's policies will be borne by the US economy and the US dollar credit [32].

Investment Rating - The report assigns a "Buy" rating for the company, with a target price of 74.76 RMB based on a 30x PE for 2026 [3]. Core Insights - The company, HIGOLD GROUP, is a leading player in the domestic furniture hardware industry, showcasing strong alpha and high profitability with a projected revenue CAGR of +35% and net profit CAGR of +76% from 2020 to 2024 [1][3]. - The furniture hardware market is estimated at 700 billion RMB, with a clear "pyramid" competitive structure where foreign brands dominate the high-end market while domestic leaders rapidly rise in the mid-to-high-end segments [1][2]. - The company has established a robust competitive moat through brand building, channel advantages, and extreme manufacturing capabilities, making its business model difficult to replicate [2]. Financial Performance - The company is expected to achieve net profits of 7.5 billion RMB, 10.0 billion RMB, and 13.1 billion RMB for the years 2025, 2026, and 2027 respectively, with a current PE ratio of 36.1, 27.3, and 20.8 for those years [3][7]. - Revenue is projected to grow from 2,222 million RMB in 2023 to 6,167 million RMB in 2027, with a revenue growth rate of 37.13% in 2023 and expected to stabilize around 30% in subsequent years [7][23]. - The company's ROE is forecasted to remain above 30%, reflecting its high return on equity driven by strong profitability and efficient asset turnover [3][36]. Industry Overview - The furniture hardware industry is a critical component of the furniture manufacturing process, with a market size exceeding 2000 billion RMB, and a specific focus on the home functional hardware market estimated at around 700 billion RMB [1][53]. - The industry is characterized by a diverse range of products, including basic hardware, functional hardware, and storage hardware, which are essential for enhancing furniture quality and functionality [50][54]. - The demand for home functional hardware is supported by stable renovation needs in the existing housing market, contributing to a solid growth outlook for the industry [1][53].