Tech Market Analysis Tools

Here are 5 machine learning algorithms used for FP&A and #finance time series analysis: ✅ ARIMA/SARIMA: Forecast future revenues and expenses by identifying trends and seasonality. ✅ LSTM: Analyze complex patterns in cash flow or sales data to improve financial planning. ✅ Prophet: Handle unpredictable markets and still make reliable forecasts. ✅ GARCH: Assess and predict market volatility to make more informed investment or budgeting decisions. More detail below ↓ 1. ARIMA (Auto-Regressive Integrated Moving Average) ARIMA helps predict future values by analyzing past data to identify patterns like trends or seasonality. For example, you can use ARIMA to forecast next year’s monthly revenue by recognizing historical trends and seasonal variations, such as higher sales during holiday seasons. 2. LSTM (Long Short-Term Memory) Networks LSTM is an artificial intelligence technique that learns from past data and remembers long-term patterns. It can be used in FP&A to forecast cash flow by identifying recurring inflows and outflows over time, like specific project payments or seasonal cash patterns. 3. SARIMA (Seasonal ARIMA) SARIMA extends ARIMA by incorporating seasonality, making it ideal for forecasting data with regular patterns. For example, you can predict quarterly expenses more accurately if certain quarters have consistently higher costs due to contracts or seasonal demand. 4. Prophet Prophet, developed by Facebook, handles missing data and outliers well, making it useful for complex datasets. To get the code and example for implement it, go here: https://lnkd.in/eJKcHzqU You could use Prophet to forecast annual sales even when your data is incomplete or affected by irregular events like economic shifts. 5. GARCH (Generalized Autoregressive Conditional Heteroskedasticity) GARCH models volatility and is great for predicting how much financial data varies over time. You can apply it in FP&A to assess and predict the volatility of stock prices in your investment portfolio, helping in risk management and budgeting.

Happy Friday everyone, this week in #learnwithmz, if you are a Product manager learning about AI this post is for you. PMs looking to get hands-on with AI side projects don’t have to be expert in AI, just a curiosity and willingness to experiment. Here’s a step-by-step guide to help you get hands-on with AI side projects. 💡 Start small: Automate Regular Tasks Identify tasks you do frequently that AI can streamline, examples: - Feedback theme collection - Feature request prioritization - Market research automation 📌 Example project: AI-Powered Market Research Assistant What is it? A tool that uses AI to gather and analyze market data, customer reviews, competitor strategies, and trending topics, delivering actionable insights for product or feature development. Why build it? - Get near real-time insights into customer needs and competitor strategies. - Accelerate decision-making for market opportunities. - Ensure your product strategy stays aligned with industry trends. Step 1 - Define Scope Inputs: - Customer reviews and feedback. - News articles or blog posts about competitors. - Social media trends and hashtags. Outputs: - Key themes in customer sentiment. - Competitor summaries. - A list of emerging trends or gaps in the market. Step 2 - Choose Tech Stack Web Scraping: BeautifulSoup or Scrapy to gather data from review sites and blogs. Sentiment Analysis: OpenAI, Hugging Face, or #Azure AI Language. Trend Analysis: Google Trends API or Twitter API. Visualization: Power BI or Streamlit. Step 3 - Build and Iterate Start simple, test test test, and refine based on feedback. I’m working on a prototype for this assistant, stay tuned for updates after the holidays. What kind of market data do you find most valuable? Let’s discuss in the comments! #ProductManagement #AI #Innovation #marketresearch P.S. Image is generated via DALL·E

Predicting #financialmarket stress has long proven to be a largely elusive goal. Advances in artificial intelligence and #machinelearning offer new possibilities to tackle this problem, given their ability to handle large datasets and unearth hidden nonlinear patterns. In the BIS paper , the authors have developed a new approach based on a combination of a recurrent neural network (RNN) and a large language model. Focusing on deviations from triangular arbitrage parity (TAP) in the Euro-Yen currency pair, our RNN produces interpretable daily forecasts of market dysfunction 60 business days ahead. To address the “black box” limitations of RNNs, our model assigns data-driven, time-varying weights to the input variables, making its decision process transparent. These weights serve a dual purpose. First, their evolution in and of itself provides early signals of latent changes in market dynamics. Second, when the network forecasts a higher probability of market dysfunction, these variable-specific weights help identify relevant market variables that we use to prompt an LLM to search for relevant information about potential market stress drivers.  - Source Bank for International Settlements – BIS

Stock price forecasting is difficult because prices are driven by many external forces, like macroeconomics, policies, company fundamentals, and investor sentiment, which make the series noisy, unstable, and hard to model. Machine- and Deep-Learning (ML/DL) are widely used for stock price analysis due to their higher predictive accuracy compared to traditional statistical and econometric methods. Although DL can capture nonlinear, high-dimensional market patterns, its effectiveness depends on having large datasets. In practice, daily stock data are limited, especially for IPOs, and standard data-augmentation techniques used in computer vision cannot be applied because they break temporal order. This creates a data-scarcity problem that weakens model performance. Beyond limited data, stock prices also contain intertwined components such as trends, cycles, and randomness. Single- or multi-scale decomposition methods break down a signal such as a time series, stock price data, or a sound wave, etc, into components, each representing the signal's behavior at a different scale or level of detail. These subseries, however remain correlated in practice. Existing models treat them independently and ignore these cross-relationships, losing valuable predictive information. To address both data limitations and structural complexity, the authors of [1] proposes a combined TimeGAN + decomposition learning framework ('TimeGAN + SSA + LSTM'). Multi-view market data (open, high, low, close, volume) are first used to train a TimeGAN model, which generates realistic synthetic sequences to expand the dataset. The closing-price series is then decomposed using SSA (singular spectrum analysis) into smoother subseries, and an LSTM extracts temporal features from each. A self-attention mechanism captures the interactions among correlated subseries, and the fused representation is further enhanced by modelling its dependencies with other market-feature series. A final LSTM produces the closing-price prediction. Experiments were conducted on nearly a decade of data from multiple international stock indices: the U.S. S&P 500 (SP500), China’s CSI 300 (CSI300), Japan’s Nikkei 225 (N225), and the U.K.'s FTSE 100 (FTSE100). The results demonstrate that the proposed 'TimeGAN + SSA + LSTM' integrated approach, combining data augmentation, decomposition, and inter-series attention, achieves superior prediction accuracy (RMSE) and superior Sharpe-Ratio (SR) compared to other advanced baselines (BP, LSTM, VMD-LSTM, N-Beats, SCINet, DLinear, MLSF and MASTER). #QuantFinance The link to the paper is available in the comments.

When it comes to AI-powered market research, it's time to challenge the conventional wisdom. Replicating human survey results is often seen as the gold standard, but what if that's not enough? Traditional surveys, and even naive AI models, tend to overstate consumer intentions, missing the mark on real-world actions. Through an experiment with Ask Rally's language models, we found that a basic model replicated survey biases (78% of simulated responses favored an eco-friendly car), yet switching to a more advanced model cut this figure to 37%, much closer to actual market behavior. The takeaway? The true advantage lies not in mirroring traditional methods but in choosing and calibrating AI models that bridge the intention-action gap. This approach not only aligns synthetic research with reality but could redefine how we predict consumer behavior altogether.

More information on [GA4] Benchmarking Overview Benchmarks are key metrics that enable you to compare your business's performance against other businesses in your industry. Google Analytics provides these benchmarks through peer groups—cohorts of similar businesses determined by factors like industry vertical and other relevant details. Key Features Daily Updates: Benchmarks are refreshed every 24 hours to provide the most current data. Eligibility Requirements: To access benchmarking data, your Google Analytics property must have the "Modeling contributions & business insights" setting enabled. Additionally, your property must generate sufficient user data to be included in a peer group. Data Protection Your benchmarking data is encrypted and protected, ensuring privacy and aggregation. There are also thresholds to guarantee that a minimum number of properties are included before benchmarks are available to a peer group. Accessing Benchmarking Metrics To view benchmarking data: Select the desired metric in the overview card on the Home page. Expand the Benchmarking category. Choose from a variety of metrics, such as Acquisition, Engagement, Retention, and Monetization. Using Benchmarking Data When benchmarking data is activated, you'll see: Your property's trendline The median of your peer group The range within your peer group (shaded area) Benchmarking comparisons are available within the 25th to 75th percentile to help you make informed decisions based on your performance relative to your peers. Changing Your Peer Group You can change your peer group to ensure more accurate comparisons. Peer groups are categorized based on industry characteristics, such as Shopping > Apparel or Travel & Transportation. Example Scenarios Acquisition: If your 'New User Rate' is below the 25th percentile, consider boosting user acquisition strategies. Engagement: A high 'Average Engagement Time per Session' could be leveraged by enhancing conversion strategies. Retention: A high 'Bounce Rate' may indicate a need for better user experience and content accessibility. Monetization: Low 'ARPU' suggests exploring strategies like upselling or personalized offers. Conclusion Benchmarking data in GA4 offers actionable insights by comparing your performance with industry peers, helping you identify strengths and areas for improvement to achieve your business goals.

Launching Benchmarking in Agent Analytics. This is the first peer level benchmark for AI Search, built on real citation bot traffic from 800,000+ pages in the Profound Network. Most heads of search can tell me their Google rank for any keyword off the top of their head. For AI Search, that kind of ranking has never existed. And without it, neither has a definition of what "good" looks like for how often your site is getting cited. Until today. Customers who opt in can now unlock: 1/ Page by page breakdowns of citation performance 2/ Drill downs by industry and company size 3/ Weekly rankings to measure progress over time Josh Blyskal explains what we've built. Learn more → https://lnkd.in/ehkS3aYu

I built something for every finance leader trying to benchmark their tech stacks. And it uses real data from peers. Seriously. There was no good way to answer a simple question: What are companies like mine actually using? Like $25M to $50M companies with 200 people…. What are they using? So I built a solution. I basically turned the CFO group chat into a live benchmarking tool, but with 1,000+ members who've shared their relevant revenue and employee ranges + current tools they use across every finance category. Here's how it works: ➡️ Input: Fill in your current set up… takes 3 minutes, 100% multiple choice ➡️ Output: Get instant benchmarks… see what CFOs at your revenue stage actually use (first party data from readers) Then you can go deeper. ➡️ Research individual vendors: adoption graphs, honest pricing estimates, implementation timelines, and where each tool starts to break down ➡️ Browse by category: dive into FP&A, expense management, AP, billing, close management, and more to see adoption patterns and how usage spreads across company sizes ➡️ See the next stage: toggle to the next stage and see what you'll probably be buying in 18 months No more "yea I mean it depends on your situation" answers. We’ve got you real data from real finance leaders, organized in a way that you can make decisions. Take the survey and get the live data immediately! https://cfotechguide.com/

💭 AI is transforming finance—but is it truly reshaping the core of Quant Finance beyond just trading? While algorithmic trading gets most of the attention, AI is making a deeper impact in risk modeling, derivatives pricing, and portfolio optimization. 1️⃣ Sentiment Analysis for Market Forecasting (LLMs & NLP Models) 👉 Why it matters: Markets don’t move on fundamentals alone—investor sentiment drives volatility. AI-powered NLP can process news, earnings calls, analyst reports, and social media to detect sentiment shifts in real time, providing traders with early signals before price movements occur. 🛠 Real Models in Action: ✔ FinBERT (Hugging Face) – A finance-focused NLP model trained on earnings reports and financial news to extract sentiment insights. ✔ GPT-4 fine-tuned for finance – Used in hedge funds to generate sentiment-based trading signals and volatility forecasts. ✔ BloombergGPT – Specialised for market-related NLP tasks, enhancing automated financial analysis. 2️⃣ AI for Derivatives Pricing & Risk Management (Deep Learning & Stochastic Models) 👉 Why it matters: Traditional pricing methods rely on Monte Carlo simulations and PDE-based models, which can be computationally expensive and slow. AI accelerates pricing and hedging strategies by learning risk-neutral representations and improving predictive accuracy for exotic derivatives. 🛠 Real Models in Action: ✔ Neural SDEs (Stochastic Differential Equations) – AI-driven models that learn underlying stochastic processes for better risk-neutral pricing. ✔ Physics-Informed Neural Networks (PINNs) – AI-enhanced solvers that significantly speed up complex derivatives pricing calculations. ✔ Deep Hedging Models – AI-powered dynamic hedging strategies that adjust in real time, outperforming traditional Black-Scholes delta hedging in volatile markets. 3️⃣ AI for Dynamic Portfolio Optimization (Reinforcement Learning & Bayesian ML) 👉 Why it matters: Traditional Mean-Variance Optimization (MVO) assumes fixed return distributions and correlations, which often break down during market shifts. AI allows adaptive asset allocation, helping investors manage risk dynamically and rebalance portfolios in response to changing market regimes. 🛠 Real Models in Action: ✔ Reinforcement Learning Portfolio Management (RLPM) – Uses deep Q-learning and policy gradient methods to find optimal asset allocation strategies under different market conditions. ✔ Bayesian Neural Networks (BNNs) – Introduces uncertainty estimation in return predictions, improving risk-aware decision-making. ✔ Hierarchical Risk Parity (HRP) – AI-powered clustering of assets for better diversification and tail-risk mitigation, outperforming classical Markowitz models. #AI #QuantFinance #MachineLearning #RiskManagement #DerivativesPricing #PortfolioOptimization #SentimentAnalysis #FinancialModeling #FinTech #HedgeFunds #MarketRisk #FinanceJobs

I’ve noticed a lack of real, factual business benchmarks in the architecture industry. Industry reports have historically been based on self-reported surveys of architecture firms. But there’s a lot of variability in how firms interpret certain metrics, which can lead to skewed and unreliable results. Plus, the data usually exists behind a paywall that charges hundreds of dollars to access. Today, we’re changing that at Monograph with the release of our 2024 Architecture Business Benchmarks Report (ABBR)! Now architecture firms can access benchmarks based on accurate data for free. The report shares bottom quartiles, medians, top quartiles, and averages across 5 key business metrics: → Net Revenue per Full-Time Equivalent → Net Cost per Full-Time Equivalent → Utilization Rate → Realization Rate → Time to Payment The ABBR uses first-party data directly from the source of Monograph’s thousands of users (completely anonymized, of course). For that reason, I believe it’s the most accurate report of business metrics ever to be shared in the architecture industry. Use it to compare your own firm’s performance so you can know, with confidence, where you stands! Link to download in the comments below ⬇️