deepmoney-67b-chat

The same data as the 34b version is used for training, and the base uses deepseek-67b.

Train method:

1. pretrain with lora r = 512
2. sft with lora r = 128

Alpaca format:

You are a senior investment expert. Please make your research and judgment on the following targets.

### Instruction:
China has instructed heavily indebted local governments to delay or halt some state-funded infrastructure projects, three people with knowledge of the situation said, as Beijing struggles to contain debt risks even as it tries to stimulate the economy.

Which industry sectors or investment targets may the above news affect?

### Response:
The above news could potentially impact several industry sectors and investment targets related to infrastructure development in China. Some affected areas might include construction companies, heavy machinery manufacturers, materials suppliers such as cement and steel producers, engineering firms, and project management service providers that have significant exposure to Chinese government-backed infrastructure projects.

Eval

You are a senior investment expert. Please make your research and judgment on the following targets.

### Instruction:
China has instructed heavily indebted local governments to delay or halt some state-funded infrastructure projects, three people with knowledge of the situation said, as Beijing struggles to contain debt risks even as it tries to stimulate the economy.

Please design a quantitative method to study the impact of the above news on the construction industry. And accordingly explain what data needs to be used.

### Response:
To quantitatively study the impact of this news on the construction industry, we can use a combination of statistical analysis and economic modeling techniques. Here's a proposed methodology along with the necessary data required for each step:

1. **Data collection**: Collect historical data related to the construction industry, including but not limited to:
	* Construction output index (COI)
	* Number of new construction permits issued
	* Investment in fixed assets (FAI) in the construction sector
	* Employment numbers in the construction industry
2. **Time series analysis**: Perform time series analysis on the collected data to identify trends and patterns over time. This will help us understand the overall performance of the construction industry before the announcement.
3. **Panel data regression**: Conduct panel data regressions using data from different provinces/regions in China. The dependent variable could be one of the indicators mentioned above (e.g., COI), while independent variables may include:
	* Province-level FAI investments in the construction sector
	* Provincial GDP growth rate
	* Housing price index (HPI) at the provincial level
4. **Event studies**: Design an event study around the date of the news announcement. Create two sets of portfolios: treated and control groups. The treated group should consist of companies directly affected by the policy change (construction firms operating primarily in regions where delayed/halted projects were concentrated). The control group would comprise similar companies that were less likely to have been impacted by the policy shift. Compare the stock returns and volatility between the two groups before and after the event.
5. **Interrupted time series analysis** (ITS): Combine the results from Steps 1-4 into an ITS framework to estimate the causal effect of the policy change on the construction industry. Use ARIMA models or other appropriate time series methods to account for seasonality, trends, and autocorrelation.
6. **Qualitative information sources**: Supplement the quantitative analysis with qualitative information gathered from company earnings calls, analyst reports, and interviews with industry experts to gain further insights into how market participants perceive the potential impacts of this news on the construction industry.
7. **Sensitivity analysis**: Test the robustness of your findings by varying key assumptions and inputs within reasonable bounds. This will help you assess whether your conclusions remain consistent under alternative scenarios.
8. **Impulse response functions** (IRFs): Estimate IRFs to determine how long any short-term shocks resulting from the policy change might last and whether there are spillover effects onto other sectors or macroeconomic variables.
9. **Counterfactual simulations**: Using structural vector autoregression (SVAR) models or other suitable approaches, simulate counterfactual scenarios to understand what would have happened to the construction industry had the policy change not occurred.

By combining these steps, you should be able to develop a comprehensive understanding of the potential impact of delaying or halting infrastructure projects on the Chinese construction industry.