Core Viewpoint - The article discusses the recent performance of the banking index, its historical context, and the current valuation, suggesting potential strategies for profit-taking as the index reaches a relatively high valuation level [6][18][21]. Group 1: Market Performance - The market experienced a slight decline after three consecutive days of increase, maintaining a rating of 4.9 stars [1]. - Both large-cap and small-cap stocks saw a decrease, while the banking index showed strength and reached a historical high [2][3][6]. - The value style, including dividend stocks, exhibited relatively small fluctuations during this period [4]. Group 2: Historical Context of Banking Index - The banking index has had strong performance in recent years, but historically, it has also faced periods of underperformance, leading to negative perceptions such as "three fools" and "big rotten smell" [6][8]. - From 2014 to 2015, small-cap stocks were in a bull market while large-cap stocks, including banks, were underperforming [7]. - The period from 2016 to 2017 saw a shift where large-cap stocks began to perform better as small-cap stocks faced declines due to valuation bubbles [8]. Group 3: Current Valuation and Profit-Taking Strategies - The banking index has seen significant growth in recent years, driven by both valuation increases and growth in earnings and net assets, resulting in a "double effect" [19]. - Currently, the banking index's valuation is considered normal to slightly high, with expectations that upcoming financial reports may lead to a decrease in perceived valuation [21]. - For profit-taking, two strategies are suggested: selling based on high valuation or achieving a satisfactory return, with recommendations for gradual selling [23]. Group 4: Dividend Indices and Value Style - The article differentiates between the banking index and dividend indices, noting that the banking index is weighted by market capitalization while dividend indices are weighted by dividend yield [10][11]. - Despite differences, both categories fall under the broader value style, which has shown strength from 2022 to 2024 [14][15].

Group 1 - The core concept of style rotation is based on the characteristics of ETFs, with common types including large-cap vs. small-cap rotation and growth vs. value rotation [1] - The logic of style rotation relies on two driving factors of equity asset prices: earnings and valuation, where earnings are the key determinant of style strength [1] - The performance difference between growth and value stocks is highly correlated with their earnings growth rate difference, indicating that when the earnings growth gap widens, growth stocks are likely to outperform value stocks [1] Group 2 - Large-cap stocks are more influenced by economic cycles due to their higher representation in the national economy, leading to stronger performance in economic upturns compared to small-cap stocks [1] - The liquidity environment significantly impacts stock valuations, with small-cap stocks being more sensitive to liquidity changes; they tend to perform better in expanding liquidity conditions, while large-cap stocks perform better when liquidity tightens [1]

Quantitative Models and Construction Methods - **Model Name**: "百元转股溢价率" (Premium Rate per 100 Yuan Conversion) **Model Construction Idea**: Compare convertible bond valuation with equity valuation using historical percentile metrics to assess relative allocation value [4][15] **Model Construction Process**: Fit a cross-sectional curve of conversion premium rate and conversion value at each time point. Substitute conversion value = 100 into the fitted formula to derive "百元转股溢价率". Formula: $$ y_{i}=\alpha_{0}+\,\alpha_{1}\cdot\,{\frac{1}{x_{i}}}+\epsilon_{i} $$ Here, \( y_{i} \) represents the conversion premium rate of the \( i \)-th bond, and \( x_{i} \) represents the conversion value of the \( i \)-th bond [44] **Model Evaluation**: Provides a relative valuation perspective for convertible bonds versus equities [15] - **Model Name**: "修正 YTM – 信用债 YTM" (Adjusted YTM Minus Credit Bond YTM) **Model Construction Idea**: Adjust convertible bond yield-to-maturity (YTM) by removing the impact of conversion clauses to compare with credit bond YTM [4][15] **Model Construction Process**: $$ \text{Adjusted YTM} = \text{Convertible Bond YTM} \times (1 - \text{Conversion Probability}) + \text{Expected Conversion Annualized Return} \times \text{Conversion Probability} $$ Conversion probability is calculated using the Black-Scholes model, incorporating stock price, strike price, stock volatility, remaining term, and discount rate. The median of the differences between adjusted YTM and credit bond YTM is then computed: $$ \text{"修正 YTM – 信用债 YTM" Median} = \text{median}\{X_1, X_2, ..., X_n\} $$ Here, \( X_i \) represents the difference between adjusted YTM and credit bond YTM for the \( i \)-th bond [45][46] **Model Evaluation**: Suitable for assessing relative allocation value between debt-heavy convertible bonds and credit bonds [15] Quantitative Factors and Construction Methods - **Factor Name**: 转股溢价率偏离度 (Conversion Premium Rate Deviation) **Factor Construction Idea**: Measure deviation of conversion premium rate from fitted values to assess valuation differences [21] **Factor Construction Process**: $$ \text{Conversion Premium Rate Deviation} = \text{Conversion Premium Rate} - \text{Fitted Conversion Premium Rate} $$ Fitted values are determined by the cross-sectional curve fitting process [21] **Factor Evaluation**: Effective in comparing valuation across different convertible bonds [21] - **Factor Name**: 理论价值偏离度 (Theoretical Value Deviation) **Factor Construction Idea**: Assess price expectation differences using Monte Carlo simulation [21] **Factor Construction Process**: $$ \text{Theoretical Value Deviation} = \frac{\text{Convertible Bond Closing Price}}{\text{Theoretical Value}} - 1 $$ Monte Carlo simulation considers conversion, redemption, downward revision, and repurchase clauses, simulating 10,000 paths at each time point using the same credit term limit rate as the discount rate [21] **Factor Evaluation**: Provides a comprehensive valuation perspective by incorporating multiple convertible bond clauses [21] - **Composite Factor Name**: 转债综合估值因子 (Convertible Bond Comprehensive Valuation Factor) **Factor Construction Idea**: Combine conversion premium rate deviation and theoretical value deviation for enhanced valuation analysis [21] **Factor Construction Process**: $$ \text{Convertible Bond Comprehensive Valuation Factor} = \text{Rank(Conversion Premium Rate Deviation)} + \text{Rank(Theoretical Value Deviation)} $$ **Factor Evaluation**: Demonstrates superior performance across various convertible bond categories [21] - **Factor Name**: 转债市场情绪捕捉指标 (Convertible Bond Market Sentiment Capture Indicator) **Factor Construction Idea**: Use momentum and volatility deviation to identify market sentiment [29] **Factor Construction Process**: $$ \text{Market Sentiment Capture Indicator} = \text{Rank(20-day Momentum)} + \text{Rank(Volatility Deviation)} $$ **Factor Evaluation**: Effective in guiding convertible bond style rotation strategies [29] Model Backtesting Results - **"百元转股溢价率" Model**: Rolling three-year percentile at 47.4%, rolling five-year percentile at 50.9% [4][15][18] - **"修正 YTM – 信用债 YTM" Model**: Current median value at -0.03% [4][15][18] Factor Backtesting Results - **转股溢价率偏离度 Factor**: Enhanced excess returns in the past four weeks for偏股,平衡,偏债 convertible bonds at 1.33%, 0.27%, and 0.04%, respectively [5][23] - **理论价值偏离度 Factor**: Demonstrates superior performance in偏股 convertible bonds [20][21] - **转债综合估值因子 Factor**: - 偏股转债低估指数: IR = 1.22, annualized return = 24.91%, annualized volatility = 20.39%, max drawdown = -22.83%, Calmar ratio = 1.09, monthly win rate = 63.64% [24] - 平衡转债低估指数: IR = 1.16, annualized return = 13.77%, annualized volatility = 11.87%, max drawdown = -16.04%, Calmar ratio = 0.86, monthly win rate = 60.23% [24] - 偏债转债低估指数: IR = 1.29, annualized return = 12.21%, annualized volatility = 9.45%, max drawdown = -17.59%, Calmar ratio = 0.69, monthly win rate = 56.82% [24] Style Rotation Backtesting Results - **转债风格轮动 Model**: - IR = 1.47, annualized return = 24.23%, annualized volatility = 16.54%, max drawdown = -15.54%, Calmar ratio = 1.56, monthly win rate = 65.91% [35] - Recent four-week return = 2.24%, year-to-date return = 26.75% [31][32]

Group 1 - Market sentiment indicator decreased to 0.8, down from 1.75, indicating a bearish outlook [10][4] - Price-volume consistency improved, but industry trends remain weak with significant capital rotation [14][4] - Total A-share trading volume increased to 1.50 trillion RMB, with daily trading volume reaching 122.514 billion shares [17][4] Group 2 - Small-cap value style is currently favored, with a notable increase in short-term trend scores for sectors like social services, non-ferrous metals, and steel [32][34] - Social services sector saw a significant short-term trend score increase of 31.25% [32][34] - The model indicates a weakening differentiation between growth and value styles, suggesting a prevailing value preference [36][37]

Core Viewpoint - The article emphasizes the rapid growth and diversification of the ETF market in China, highlighting its increasing importance as a flexible investment tool for both fund and stock investors [1][6]. Group 1: Advantages of ETFs - ETFs utilize a real-time trading mechanism that supports T+0 cross-border trading, significantly enhancing trading flexibility compared to QDII off-market funds [4]. - They passively track benchmark indices, employing strategies to control tracking error, resulting in net asset value movements closely aligned with the underlying index, such as the CSI 300 ETF [4]. - ETFs offer high transparency, with daily disclosures of subscription and redemption lists, providing timely insights into constituent stocks and their weights [4]. - Cost control is a notable advantage, with explicit fees ranging from 0.15% to 0.5% per year and trading commissions at 0.1% to 0.3%, alongside low implicit costs [4]. - The product system is diverse, covering various asset classes including stocks, bonds, and commodities, with broad-based ETFs achieving industry balance and thematic ETFs targeting specific sectors like chip design and automotive [4]. Group 2: Growth of ETF Market - By the end of 2024, the total number of ETFs in China reached 1,033, with a total scale exceeding 3.7 trillion yuan, marking an 81% increase from 2023, with a net increase of 1.7 trillion yuan [7]. - Stock ETFs accounted for 2.89 trillion yuan, representing 78% of the market, driven by policy support and significant capital inflows [7]. - Bond ETFs exceeded 170 billion yuan, with a 100% growth in scale and a 243% increase in share, influenced by loose monetary policy and declining interest rates [7]. - Commodity ETFs saw nearly 150% growth, primarily due to rising gold prices and increased demand for safe-haven assets, with gold ETFs making up over 80% of this category [7]. - As of February 2025, the total scale of ETFs further increased to 3.79 trillion yuan, with ongoing focus on broad-based and strategic ETFs, driven by policy fee reductions and product innovations [7]. Group 3: Innovations in ETFs - In 2024, new "A series" indices were created, expanding the coverage of thematic ETFs across various industries, including automotive, petrochemicals, telecommunications, and computing [9]. - The introduction of chip-related ETFs on the Sci-Tech Innovation Board addressed the lack of investment options in the chip sector, with new indices launched to cover chip design and semiconductor materials [10]. - The launch of the first ETF linked to the Hong Kong Stock Connect automotive industry index improved investment tools for investors targeting new energy vehicle companies [11]. - By February 28, 2025, the number of indices covered by ETFs expanded by eight, including innovative and high-value indices like the National Index Free Cash Flow and the Shanghai Stock Exchange Sci-Tech Innovation Board Composite Index [11].

Group 1 - The overall market trend is expected to show a fluctuating adjustment pattern in June, with limited short-term adjustment space but potentially prolonged volatility [4][60] - The A-share market is anticipated to experience a strong adjustment, while the Hong Kong stock market may perform better due to healthier chip structures, exhibiting wide fluctuations [4][60] - In early June, the dividend style is expected to outperform, while growth sectors may be relatively weak; however, from mid-June, growth styles may gain relative advantages [4][60] Group 2 - The US stock market is projected to continue its fluctuations, with risk trend models indicating high risk levels; factors such as international trade court rulings and Trump policies will influence market sentiment [4][61] - The gold market is expected to maintain a medium risk level, with no significant overvaluation or undervaluation, and is likely to strengthen gradually, forming a reverse hedging relationship with US stocks [4][61] - The bond market is anticipated to remain in a narrow fluctuation pattern, with the interest rate center potentially rising due to short-term supply pressure, but the overall downward trend remains unchanged [4][60] Group 3 - The fund allocation recommendation suggests a relatively balanced configuration, anticipating a fluctuating adjustment market, and advising to wait for the right timing [4][60] - The equity macro-micro monthly low-frequency timing model indicates a score of 0 for June, suggesting a strong adjustment pattern, with historical data showing high win rates at this score [31][30] - The model evaluates the market based on five dimensions: fundamentals, liquidity, international factors, valuation, and technical aspects, with a clear view of changes in each dimension [30][37]

- Model Name: Size Rotation Model; Construction Idea: The model aims to rotate between large-cap and small-cap stocks based on macroeconomic indicators, market conditions, and micro characteristics; Construction Process: The model uses HA large-cap and small-cap as proxy variables, and the excess return relative to the equal-weighted benchmark is calculated. The formula is: $ \text{Excess Return} = \text{Portfolio Return} - \text{Benchmark Return} $; Evaluation: The model effectively captures the rotation between large-cap and small-cap stocks, providing significant excess returns[3][12][18] - Model Name: Value-Growth Rotation Model; Construction Idea: The model rotates between value and growth stocks based on macroeconomic indicators, market conditions, and micro characteristics; Construction Process: The model uses HA value and growth as proxy variables, and the excess return relative to the equal-weighted benchmark is calculated. The formula is: $ \text{Excess Return} = \text{Portfolio Return} - \text{Benchmark Return} $; Evaluation: The model effectively captures the rotation between value and growth stocks, providing significant excess returns[15][23][26] Model Backtest Results - Size Rotation Model, Annualized Excess Return: 11.1%, IR: 1.40, Monthly Win Rate: 64.43%[18] - Value-Growth Rotation Model, Annualized Excess Return: 18.73%, IR: 2.02, Monthly Win Rate: 70.47%[23] Annual Performance of Size Rotation Model - 2013: Excess Return: 11.99%, IR: 2.3594, Monthly Win Rate: 66.67%[21] - 2014: Excess Return: 45.30%, IR: 6.7613, Monthly Win Rate: 75.00%[21] - 2015: Excess Return: 62.95%, IR: 4.6008, Monthly Win Rate: 91.67%[21] - 2016: Excess Return: 0.53%, IR: 0.0784, Monthly Win Rate: 58.33%[21] - 2017: Excess Return: 17.82%, IR: 2.8949, Monthly Win Rate: 83.33%[21] - 2018: Excess Return: 1.62%, IR: 0.2670, Monthly Win Rate: 41.67%[21] - 2019: Excess Return: 0.09%, IR: 0.0171, Monthly Win Rate: 58.33%[21] - 2020: Excess Return: 7.50%, IR: 1.2650, Monthly Win Rate: 58.33%[21] - 2021: Excess Return: 20.48%, IR: 2.5886, Monthly Win Rate: 75.00%[21] - 2022: Excess Return: 4.01%, IR: 0.5426, Monthly Win Rate: 50.00%[21] - 2023: Excess Return: 14.12%, IR: 2.9028, Monthly Win Rate: 83.33%[21] - 2024: Excess Return: -20.37%, IR: -1.5954, Monthly Win Rate: 33.33%[21] - 2025 (up to May 30): Excess Return: 5.62%, IR: 0.7109, Monthly Win Rate: 60.00%[21] Annual Performance of Value-Growth Rotation Model - 2013: Excess Return: 10.61%, IR: 1.1641, Monthly Win Rate: 58.33%[25] - 2014: Excess Return: 27.37%, IR: 3.2529, Monthly Win Rate: 66.67%[25] - 2015: Excess Return: 16.46%, IR: 1.2072, Monthly Win Rate: 66.67%[25] - 2016: Excess Return: 9.96%, IR: 1.2172, Monthly Win Rate: 83.33%[25] - 2017: Excess Return: 14.23%, IR: 2.1003, Monthly Win Rate: 66.67%[25] - 2018: Excess Return: 18.34%, IR: 2.5083, Monthly Win Rate: 91.67%[25] - 2019: Excess Return: 17.04%, IR: 2.8518, Monthly Win Rate: 75.00%[25] - 2020: Excess Return: 37.05%, IR: 3.9631, Monthly Win Rate: 66.67%[25] - 2021: Excess Return: 47.65%, IR: 3.9594, Monthly Win Rate: 83.33%[25] - 2022: Excess Return: 19.46%, IR: 1.6708, Monthly Win Rate: 83.33%[25] - 2023: Excess Return: 10.82%, IR: 1.7148, Monthly Win Rate: 66.67%[25] - 2024: Excess Return: 1.32%, IR: 0.1561, Monthly Win Rate: 41.67%[25] - 2025 (up to May 30): Excess Return: 1.27%, IR: 0.1259, Monthly Win Rate: 60.00%[25]

Core Viewpoint - The article discusses the cyclical nature of the stock market, emphasizing the rotation of styles and the inevitable return to value after periods of overvaluation in certain sectors [2]. Market Trends and Historical Context - In 2012-2013, small-cap stocks and the ChiNext index saw significant gains, while large-cap stocks were undervalued with a P/E ratio below 10 times [2]. - The second half of 2014 witnessed a rapid increase in large-cap stocks led by brokerage firms, while the ChiNext index remained stagnant [2]. - In 2015, the market shifted back to growth stocks, with the ChiNext index experiencing a 150% increase over four months, despite large-cap stocks remaining flat [2][3]. - The market peaked in May 2015, leading to a significant downturn with many stocks hitting their lower limits [3]. - From 2016 to 2018, overvalued growth stocks faced a three-year decline, while large-cap stocks began a small bull market, with leading banks reaching a valuation of 10 times [3]. - The market saw a downturn in 2018, with the ChiNext index suffering substantial losses [3]. - Between 2019 and 2021, strong stocks in sectors like oil and banking faced declines, while growth stocks in consumption, pharmaceuticals, and technology surged, with the ChiNext index rising by 200% [3]. - 2022 was another down year, but by early 2023, value stocks in banking, telecommunications, and oil began to lead the market again, with many doubling in value [3]. Future Outlook - By May 2025, the market shows signs of potential shifts, with banks, telecommunications, and oil stocks having doubled, but some are experiencing declining performance [4]. - Leading companies in consumption and manufacturing have seen their dividend yields drop below 4% or even 5% due to declines or growth [4]. - The pharmaceutical sector, which has faced a four-year decline, is beginning to stabilize, with new consumption trends emerging and significant breakthroughs in drug development [4]. - The market is seeing an influx of new capital, with state-owned enterprises supporting the market, insurance funds investing in high-dividend stocks, and speculative funds driving up small-cap stocks [4].

Quantitative Models and Construction Methods Model Name: Convertible Bond Valuation Model - **Construction Idea**: The model aims to compare the valuation of convertible bonds with their underlying stocks and other credit bonds to determine relative investment value[4][13] - **Construction Process**: - **Convertible Bond and Stock Valuation**: Construct the "Hundred Yuan Conversion Premium Rate" to compare the valuation of convertible bonds and stocks over time. Calculate the rolling historical percentile to measure the relative configuration value[4][13] - Formula: $$ y_{i}=\alpha_{0}+\,\alpha_{1}\cdot\,{\frac{1}{x_{i}}}+\epsilon_{i} $$ where \( y_{i} \) is the conversion premium rate of the i-th bond, \( x_{i} \) is the conversion value of the i-th bond[46] - **Convertible Bond and Credit Bond Valuation**: Focus on the impact of conversion terms on the yield to maturity (YTM) of convertible bonds, and calculate the "Adjusted YTM - Credit Bond YTM" median to measure the relative configuration value between convertible bonds and credit bonds[4][13] - Formula: $$ \text{Adjusted YTM} = \text{Convertible Bond YTM} \times \text{Maturity Probability} + \text{Expected Conversion Yield} \times \text{Conversion Probability} $$ $$ = \text{Convertible Bond YTM} \times (1 - \text{Conversion Probability}) + \text{Expected Conversion Yield} \times \text{Conversion Probability} $$ $$ \text{Adjusted YTM - Credit Bond YTM Median} = \text{median}\{X_1, X_2, ..., X_n\} $$ where \( X_i \) represents the difference between the adjusted YTM of the i-th convertible bond and the YTM of a credit bond of the same grade and maturity[47][48] - **Evaluation**: The model provides a systematic approach to evaluate the relative investment value of convertible bonds compared to their underlying stocks and other credit bonds[4][13] Model Name: Convertible Bond Style Rotation Model - **Construction Idea**: The model captures market sentiment using momentum and volatility deviation indicators to construct a convertible bond style rotation portfolio[5][23] - **Construction Process**: - **Market Sentiment Indicators**: Use convertible bond 20-day momentum and volatility deviation as market sentiment capture indicators[5][23] - Formula: $$ \text{Convertible Bond Style Market Sentiment Capture Indicator} = \text{Rank}(\text{Convertible Bond 20-day Momentum}) + \text{Rank}(\text{Volatility Deviation}) $$ - **Portfolio Construction**: Rank the convertible bond style indices based on the sentiment indicators, and allocate the portfolio based on the rankings. If all three styles are selected, invest 100% in the balanced low valuation style[5][23][32] - **Evaluation**: The model effectively captures market sentiment and adjusts the portfolio allocation to optimize returns[5][23] Model Backtest Results Convertible Bond Valuation Model - **Hundred Yuan Conversion Premium Rate**: Rolling three-year percentile at 43.5%, rolling five-year percentile at 49.8%[4][13][16] - **Adjusted YTM - Credit Bond YTM Median**: Current median at 0.11%[4][13][16] Convertible Bond Style Rotation Model - **Recent 4-week Returns**: Convertible bond style rotation return at 8.58%, year-to-date return at 23.98%[5][33][35] - **Information Ratio**: Convertible bond style rotation IR at 1.46, convertible bond low valuation equal-weight index IR at 1.22, convertible bond equal-weight index IR at 0.71[38] Quantitative Factors and Construction Methods Factor Name: Convertible Bond Comprehensive Valuation Factor - **Construction Idea**: Combine the deviation of conversion premium rate and theoretical value deviation (Monte Carlo model) to construct a comprehensive valuation factor[5][24] - **Construction Process**: - **Conversion Premium Rate Deviation**: - Formula: $$ \text{Conversion Premium Rate Deviation} = \text{Conversion Premium Rate} - \text{Fitted Conversion Premium Rate} $$ - **Theoretical Value Deviation (Monte Carlo Model)**: - Formula: $$ \text{Theoretical Value Deviation} = \frac{\text{Convertible Bond Closing Price}}{\text{Theoretical Value}} - 1 $$ Monte Carlo model simulates 10,000 paths at each time point, using the same credit term limit rate as the discount rate to calculate the theoretical value of the convertible bond[25] - **Comprehensive Valuation Factor**: - Formula: $$ \text{Convertible Bond Comprehensive Valuation Factor} = \text{Rank}(\text{Conversion Premium Rate Deviation}) + \text{Rank}(\text{Theoretical Value Deviation (Monte Carlo Model)}) $$ - **Evaluation**: The factor provides a robust method to evaluate the valuation of convertible bonds comprehensively[5][24] Factor Backtest Results Convertible Bond Comprehensive Valuation Factor - **Recent 4-week Returns**: Low valuation factor enhanced excess returns in convertible bonds: 1.56% for equity-biased, 0.10% for balanced, 0.18% for debt-biased[27] - **Information Ratio**: Equity-biased convertible bond low valuation index IR at 1.22, balanced convertible bond low valuation index IR at 1.16, debt-biased convertible bond low valuation index IR at 1.29[28] Convertible Bond Low Valuation Index Components Equity-biased Convertible Bond Low Valuation Index Components - **Components**: Guangda Convertible Bond, Jindan Convertible Bond, Jingdang Convertible Bond, etc.[6][43] Balanced Convertible Bond Low Valuation Index Components - **Components**: Liqun Convertible Bond, Hebang Convertible Bond, Ying 19 Convertible Bond, etc.[6][44] Debt-biased Convertible Bond Low Valuation Index Components - **Components**: Dongnan Convertible Bond, Shunbo Convertible Bond, Huitong Convertible Bond, etc.[6][45]

Group 1 - The market is expected to continue its oscillation in the short to medium term, with accelerated style rotation driven by monetary policy easing and strong export performance [1] - In May, a rotation pattern of "risk aversion - consumption - growth" may re-emerge, starting with technology growth stocks, followed by a shift towards defensive assets as macroeconomic risks increase [1] - The recovery of the consumption sector is anticipated after the defensive phase, supported by policy dividends and improving consumption data, leading to new investment opportunities driven by domestic demand [1] Group 2 - The market shows resilience due to dual drivers of policy support and economic recovery, despite short-term fluctuations in trading volume reflecting cautious sentiment [2] - The first quarter saw a positive turnaround in net profit growth for all A-shares, indicating improving corporate earnings and strengthening internal economic recovery [2] - The effects of monetary policy easing and long-term capital inflows are expected to enhance liquidity, supporting a continued recovery in consumption and investment sectors [2]