Finance
Sagar Doshi of Nuvama recommends buying these three stocks tomorrow
Stock Market News: The Indian stock market benchmark indices, Sensex and Nifty 50, began the financial year 2024-2025 on a positive note. Both the frontline indices gained over a percent in the month of April.
On Tuesday, the domestic equity indices succumbed to fag-end selloff and ended lower for the day. The benchmark Nifty 50 and Bank Nifty hit their record high on April 30.
The Sensex ended 188.50 points, or 0.25%, lower at 74,482.78, while the Nifty 50 settled 38.55 points, or 0.17%, lower at 22,604.85.
Investors now watch out for the US Federal Reserve meeting outcome for further clues on interest rate cuts.
The Indian stock market is closed on Wednesday, May 1, on account of Maharashtra Day.
Read here: Share market holidays 2024: Is Indian stock market closed on 1st May?
Nifty 50 Outlook by Sagar Doshi
Nifty hit a fresh all time high on the last trading day of calendar month – April 2024 ending with MTD gains of 1.24%. A huge round of short covering was seen on index futures from the FII desk, where they cut the short position from 99,000 to less than half of 45,000 contracts.
Initial targets of 22,700+ have been complete and Nifty could consolidate between 22,550 and 22,800 for this truncated week. Any breakdown below 22,550 is likely to allow further negative views on the index. For now a range bound view is likely to play out for the week to come while broader markets are likely to steal the show on the buying front, said Sagar Doshi, Senior Vice President- Research, Nuvama Professional Clients Group.
Also Read: April Market Review: Nifty 50 soars for 3rd straight month, gains 1.2%; metal index top performer
Bank Nifty Outlook
Bank Nifty dropped close to 750 points from its intraday highs in the last hour of trade on Tuesday, negating its outperformance of this week over the Nifty. Yesterday’s price action suggests that an underperformance of Bank Nifty over the Nifty is likely to continue for the next couple of trading sessions which is likely to drag the index lower towards 48,600 odd, Doshi said.
Erosion of futures premium in the start of fresh derivative series is also suggesting some cool off on long positions for the index. Bank Nifty has also completed its Fibonacci Extension targets of 49,800 and faced rejection from the same. All of these point towards an underperformance for the coming week on the index, he added.
Also Read: Stocks to buy or sell: Sumeet Bagadia recommends 5 breakout stocks for tomorrow
Top Stock Recommendations by Sagar Doshi
On top stock recommendations, Sagar Doshi has recommended three stocks for tomorrow – L&T Finance, Prestige Estates Projects and Lupin.
L&T Finance | BUY | Stop Loss: ₹161.00 | Target: ₹179.00
L&T Finance shares witnessed a change in trend early 2023 as the stock gave a breakout from the trendline active since the all-time high. Since then, all swing breakouts have resulted in a favorable trade. L&T Finance stock has also been an outperformer in the sector. A swing breakout with a rise in volumes indicates the reinforcement of bullish momentum.
Prestige Estates Projects | Buy | Stop Loss: ₹1,330 | Target: ₹1,475
Prestige Estates Projects shares have registered a fresh all time high close for itself. Momentum indicator has also crossed its previous swing high indicating bullish momentum in the stock.
Lupin | Buy | Stop Loss: ₹1,587.00 | Target: ₹1,760
Lupin share price ended its 1 month consolidation as prices closed above ₹1,640 for the first time since mid-March. A positive cross over in momentum indicator affirms this bullish swing is likely to continue further.
Disclaimer: The views and recommendations made above are those of individual analysts or broking companies, and not of Mint. We advise investors to check with certified experts before making any investment decisions.
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Published: 01 May 2024, 08:08 AM IST
In a world of interconnected financial markets, policymakers and regulators face the complex task of identifying and addressing risks before they escalate into crises. The 2008-09 global crisis and recent episodes of market dysfunction highlight the need for early warning tools to detect vulnerabilities in real time. However, predicting financial market stress remains challenging, as traditional econometric models often fail to capture the complex, nonlinear dynamics and interconnectedness of modern financial systems.
Recent advances in artificial intelligence (AI) provide new tools to address these challenges. AI methods excel at analysing high-dimensional datasets and uncovering hidden patterns. While they are widely applied in asset pricing (Kelly et al. 2024), they are increasingly used for financial stability monitoring (Fouliard et al. 2021, du Plessis and Fritsche 2025). However, the ‘black box’ nature of AI models has limited their ability to generate actionable policy insights.
This article highlights recent research (Aldasoro et al. 2025, Aquilina et al. 2025) that advances the deployment of AI tools to anticipate financial market stress. These studies demonstrate the potential of AI to forecast market stress and dysfunction, offering both methodological innovations and actionable insights for policymakers by addressing the black-box issue.
The challenge of anticipating financial market stress
Financial market stress can take many forms, including liquidity shortages, price dislocations, and breakdowns in arbitrage relationships. Events such as the 1998 LTCM crisis, the 2008-09 global crisis, and the 2020 ‘dash for cash’ highlight the systemic risks posed by market dysfunction. These disruptions often begin in specific market segments, such as foreign exchange or money markets, but can quickly spread throughout the financial system, threatening its stability. Increasingly, stress has also shifted from traditional banks to non-bank financial intermediaries, reflecting the evolving nature of financial intermediation.
Traditional early warning systems, which were primarily designed to predict full-blown crises, have had mixed success. These models often suffer from high false positive rates and struggle to account for the nonlinear interactions and feedback loops that amplify shocks during periods of stress.
Machine learning (ML) offers a promising alternative, particularly for generating early warning signals. Unlike traditional models, ML algorithms can process vast datasets, identify complex relationships, and adapt to changing market conditions. The studies discussed here demonstrate the potential of these tools to anticipate market stress and provide policymakers with timely warnings.
Using machine learning to model the tail behaviour of financial market conditions
Aldasoro et al. (2025) present a novel framework for predicting financial market stress using machine learning. The study begins by constructing market condition indicators (MCIs) for three key US markets critical to financial stability: Treasury, foreign exchange, and money markets. These indicators (illustrated in Figure 1) capture dislocations in liquidity, volatility, and arbitrage conditions.
Figure 1 Market condition indices for US Treasury, foreign exchange, and money markets
Notes: This figure shows the five-day moving average of market condition indices for the US Treasury, money, and foreign exchange (FX) markets (upper, middle, and lower panels respectively). The sample period is from 01/01/2003 to 31/05/2024.
The paper employs random forest models, a popular tree-based machine learning algorithm, to forecast the full distribution of future market conditions. This approach uses multiple decision trees and averages their predictions, reducing the risk of overfitting. The results are noteworthy: random forest models outperform traditional time-series approaches, particularly in predicting tail risks over longer time horizons (up to 12 months). This is especially evident in forecasting foreign exchange market conditions (Figure 2).
Figure 2 Forecast accuracy of random forest and autoregressive models
Notes: This figure compares quantile losses between the random forest and autoregressive models based on out-of-sample predictions across forecast horizons. Negative values indicate better performance of the random forest model.
To address the black-box issue, the study uses Shapley value analysis to explain the main factors driving market stress predictions. The analysis reveals that macroeconomic expectations and uncertainty, particularly around monetary policy, are significant contributors to market vulnerability. Liquidity conditions and the state of the global financial cycle also play critical roles. This approach not only improves predictive accuracy but also provides actionable insights for policymakers, enabling them to respond proactively to the build-up of vulnerabilities.
Combining machine learning with large language models
Aquilina et al. (2025) take a different approach by integrating numerical data with textual information using large language models (LLMs). The study focuses on deviations from triangular arbitrage parity (TAP) in the euro-yen currency pair, a key indicator of dysfunction in the foreign exchange market. By combining recurrent neural networks (RNNs) with LLMs, the authors develop a two-stage framework to forecast market stress and identify its underlying drivers.
The recurrent neural network detects periods of heightened triangular arbitrage parity deviations up to 60 working days in advance, effectively predicting market dysfunctions that may occur within a one-month window. Out-of-sample testing on 3.5 years of data demonstrates the model’s practical value. For example, the model identified elevated risks before the March 2023 banking turmoil, despite being trained only on data up to the end of 2020 (Figure 3). However, it did not predict the market anomaly caused by the onset of COVID-19, as the event’s origins were external to the financial system.
Figure 3 Predictive accuracy of market dysfunction episodes
Notes: True data: 20-day average of the daily euro-yen triangular arbitrage parity difference with the US dollar as the vehicle currency, calculated on a minute-by-minute basis. The vertical red dashed line represents the end of the training period, end-2020; everything to the right of this line is considered pseudo out-of-sample.
To address the black-box challenge, Aquilina et al. (2025) develop a new architecture for recurrent neural network models that dynamically assigns weights to input variables. This allows the model to identify which indicators are most important for predicting future market conditions at any given time. These weights can then be fed into an LLM to search financial news and commentary for contextual information, helping to uncover potential triggers of market stress.
For instance, during the March 2023 banking turmoil, the model flagged elevated risks in euro liquidity and cross-currency arbitrage. Guided by these signals, the LLM identified news articles discussing tightening dollar funding conditions and rising geopolitical tensions. This targeted approach transforms opaque statistical forecasts into narrative explanations that policymakers can understand and act upon.
Policy implications and conclusions
While much more research into these issues is needed, these approaches show the promise of leveraging AI tools for financial stability monitoring and analysis.
- First, our work has shown that machine learning models are useful in forecasting future conditions of various markets.
- Second, the integration of numerical and textual data through machine learning and large language models provides a richer understanding of market dynamics. Policymakers can use these tools to monitor emerging risks in real time, combining quantitative forecasts with qualitative insights from financial news and commentary.
- Finally, the interpretability of machine learning models is critical for their adoption in policy settings. Techniques like Shapley value analysis and variable-specific weighting not only improve the transparency of forecasts but also provide actionable information about the drivers of market stress.
Overall, these approaches represent a significant step forward in leveraging AI to detect vulnerabilities in financial markets. By combining different methods, the studies offer novel tools for forecasting market stress and understanding its underlying drivers. However, these methods are not without limitations, such as the risk of overfitting and the need for substantial computational resources. Policymakers and regulators should invest in the necessary data and infrastructure to fully harness the potential of these tools.
References
Aldasoro, I, P Hördahl and S Zhu (2022), “Under pressure: market conditions and stress”, BIS Quarterly Review (19): 31–45.
Aldasoro, I, P Hördahl, A Schrimpf and X S Zhu (2025), “Predicting Financial Market Stress with Machine Learning”, BIS Working Papers No. 1250.
Aquilina, M, D Araujo, G Gelos, T Park and F Pérez-Cruz (2025), “Harnessing Artificial Intelligence for Monitoring Financial Markets”, BIS Working Papers No. 1291.
Du Plessis, E and U Fritsche (2025), “New forecasting methods for an old problem: Predicting 147 years of systemic financial crises”, Journal of Forecasting 44 (1): 3-40.
Fouliard, J, M Howell, H Rey and V Stavrakeva (2021), “Answering the queen: Machine learning and financial crises”, NBER Working Paper 28302.
Huang, W, A Ranaldo, A Schrimpf and F Somogyi (2025), “Constrained liquidity provision in currency markets”, Journal of Financial Economics 167: 104028.
Kelly, B, S Malamud and K Zhou (2024), “The Virtue of Complexity in Return Prediction”, Journal of Finance 79: 459-503.
Pasquariello, P (2014), “Financial Market Dislocations”, Review of Financial Studies 27(6): 1868–1914.
US-based bank holding company First Financial Bancorp has completed its previously announced acquisition of Westfield Bancorp and its subsidiary, Westfield Bank.
In June, First Financial agreed to acquire Westfield Bancorp from Ohio Farmers Insurance Company in a cash-and-stock deal valued at $325m.
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With the addition of Westfield Bank, First Financial’s total assets now stand at $20.6bn, strengthening its presence in the Midwest region of the US.
The acquisition is said to expand First Financial’s commercial banking and wealth management services in Northeast Ohio.
Westfield Bank’s retail locations and related services will now operate as part of First Financial’s network.
These branches will retain their current branding until the completion of conversion process, which is expected to occur in March 2026.
The conversion will merge the two banks’ products, processes and operating systems.
Westfield Bank clients will continue to receive services through existing channels, and will receive information about account conversions in the coming months.
First Financial president and CEO Archie Brown said: “This is an exciting step in the growth of First Financial, as the addition of Westfield Bank opens new possibilities for growth and profitability for us in an attractive market.
“We can now bring our wide range of solutions in consumer, commercial, specialty lending and wealth management to new clients, while expanding our geographic footprint for our current clients.
“The First Financial team is thrilled to welcome the Westfield Bank team members to the First Financial family.”
The transaction follows First Financial’s recent expansion activities in the Midwest.
In 2023, the company established a commercial lending presence in Northeast Ohio.
Earlier this year, First Financial announced BankFinancial, the parent company of BankFinancial, National Association, in Chicago, Illinois.
Furthermore, the company has also established a commercial banking presence in Grand Rapids, Michigan.
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