fix this latext code

\newpage
\appendix
\section*{Appendix}

\section{Industry-wise Market Research and Business Opportunities}
\label{app:MarketResearch}
\subsection{Venture Capitalists}
Venture capitalists (VCs) are investors who provide capital to startups and other businesses with the potential for rapid growth. VC as an industry is a highly data driven industry, with analysts having to analyse huge amounts of data daily. This data is analysed for:
\begin{itemize}
    \item Identifying the booming sectors based on financial data and news sentiments
    \item Identifying startups which have either an innovative solution for a problem or a well defined business strategy
\end{itemize}

\paragraph{Status Quo: }
Almost all VCs are jumping onto the data driven approach for sector analysis. AI solutions can help these VCs seive through this enormous amount of data. Current solutions, which do exist look like:
\begin{itemize}
    \item \textbf{Affinity} - It is a customer relationship management software which provides deep people and company data to understand the best startups to invest in. It, however, does not solve the problem of background checking in an efficient manner.
    \item \textbf{Moonfire} - Moonfire, a UK-based Venture Capital, relies on a proprietary AI algorithm to spot fintech, gaming, and healthcare companies across Europe. Their basic approach lies in making embeddings of their investment thesis for a particular sector. The solutions of startups is then compared with this thesis, so as to filter out companies which are in line with Moonfire's ideologies. They also argue that AI algorithms can reduce human biases by evaluating startups based on predetermined criteria. This promotes a fairer and more objective investment process, enabling founders from diverse backgrounds and under served regions to access funding opportunities based on merit.
\end{itemize}

A lot of large scale VCs have built in-house solutions for this company filtering process, but no global solution exists. Most Small scale VCs still don't have access to a reliable solution. 

\paragraph{Novel Solution:}
Our model shall generate an initial brief report about a particular company on the basis of all data fed to it regarding the company. This report will provide the VC analyst with a general understanding of the company, its sector, vision and approach, and a basic idea of the founders.

Once the VC has gotten this general overview of the company, they can ask tailored questions about say the company's vision or the details of the founder of the company. This approach allows the analyst to get as much background data as possible about the company through cross questioning, while reducing the net redundant information they have to read through. 

\paragraph{Pathway Integrations and USP}
Pathway serves as the perfect framework for our approach. It's 300+ data connectors allows us to analyse data of all formats - text filings and term sheets, PDFs of financial statements, PPTs to news headlines, tweets etc. This allows our model to get access to maximum data from varied sources.
Further, given how frequently data keeps updating in this industry, the ability to access live data is of paramount importance. This is enabled using Pathway's live data streaming ability because of it's Dynamic Vector Store.

\subsubsubsection{TAM, SAM, SOM}

For this analysis, we divide Venture Capitals in 3 categories: large scale, medium scale, and small scale. We analyse the recent investments patterns of VCs from each of these categories in the domain of AI.

\begin{itemize}
    \item Large-scale venture capital (VC) firms are dedicating an increasing portion of their capital to artificial intelligence (AI) research and development with recent data showing around 20–25\% of these firms' portfolios.
    \item Mid-sized firms generally allocate around 10\% to 15\% of their investments to AI-focused initiatives.
    \item Small-scale venture capital (VC) firms typically allocate a modest share of their funds to AI, with most setting aside around 5-10\% of their capital for AI-focused investments.
\end{itemize}


\begin{figure*}[ht]
    \centering
    \includegraphics[width=\linewidth]{Graphics/VC_SOM.png}
    \caption{TAM-SAM-SOM analysis for VCs}
    \label{fig:enter-label}
\end{figure*}

\subsection{Investment Banks (Mergers and Acquisitions)}
Investment Banks act as intermediaries  in transactions like Mergers and Acquisitions, helps in Initial Public Offering (IPO) launch preparation and act  Financial Advisors. All these tasks require a huge amount of research, data analysis, document understanding and evaluation, which places our solution at a unique place to semi-automate these tasks. 

\paragraph{Status Quo and Competitors}
Along with research and analysis conducted by Financial Advisors and other employees, following tools are used in status quo:
\begin{enumerate}
\item \textbf{FirmRoom:} FirmRoom provides a Data Room for Startups, VCs, IBs, Private Equity Firms and other Cooperates to manage their data in a structured format, generate analytics,  manage timelines and manage roles within organizations.
\item \textbf{DealRoom:} Streamlines Mergers and Acquisitions via project centralization, and provides a platform to centralize deal-related data, files, communications, and reporting. It's AI Due Diligence platform utilizes AI to create custom summaries and generate a chatbot-type environment for ease of access to information.

\item \textbf{PitchBook:} Pitchbook provides AI-driven insights on market trends and company performance, Suggests relevant companies, deals, or contacts based on user activity, Extracts critical information from vast datasets, and Automates classification and risk analysis to support strategic decisions. 

\item \textbf{AlphaSense:} AlphaSense leverages AI to enhance market intelligence by using natural language processing (NLP) for precise search capabilities, extracting key insights from vast datasets. Its machine learning models provide trend analysis and contextual relevance for search results, enabling users to quickly discover critical information and patterns. These tools support faster, more informed decision-making.
\end{enumerate}

\paragraph{Novel Solution}
Agents shall be developed for Pre-Acquisition Analysis, Processes during the Acquistion process, and analyzing post transaction efficiency. Agents shall be implmented for the following purposes:
\begin{enumerate}
    \item \textbf{Strategic Fit} \\
    \textbf{Objective:} Assess how well the target company’s objectives align with the acquiring-company’s long-term strategic goals. \\
    \textbf{Evaluation:} Generate detailed reports that weigh the pros and cons for each company, outlining areas where the acquiring company can address existing challenges within the target company.
    
    \item \textbf{Financial Performance} \\
    \textbf{Objective:} Review historical and projected financial performance. \\
    \textbf{Metrics:} Focus on indicators such as revenue growth, profitability, cash flow, and debt levels to gauge the target’s financial health.
    
    \item \textbf{Valuation} \\
    \textbf{Methods:} Apply valuation techniques like Discounted Cash Flow (DCF), market comparables, and precedent transactions. \\
    \textbf{Synergies:} Factor in potential synergies to understand their impact on the overall valuation.
    
    \item \textbf{Cultural Compatibility} \\
    \textbf{Objective:} Evaluate the cultural alignment between the two companies to predict potential integration issues. \\
    \textbf{Considerations:} Examine similarities and differences in corporate culture to assess potential operational impacts.
    
    \item \textbf{Regulatory and Legal Considerations} \\
    \textbf{Legal Compliance:} Assess the merger control regulations and identify potential legal barriers that may affect the acquisition.
    
    \item \textbf{Management and Operational Integration} \\
    \textbf{Planning:} Develop a strategy for integrating management teams and operational processes. \\
    \textbf{Resources:} Outline resources required to facilitate a smooth transition and efficient integration of operations.
    
    \item \textbf{Market Conditions} \\
    \textbf{Market Analysis:} Examine current market trends, industry dynamics, and competitive landscapes. \\
    \textbf{Impact:} Consider how the acquisition might influence the competitive positioning of the merged entity.
    
    \item \textbf{Risk Assessment} \\
    \textbf{Objective:} Identify financial, operational, and market-related risks. \\
    \textbf{Mitigation:} Formulate strategies to mitigate these risks for a more secure transaction.
    
    \item \textbf{Due Diligence} \\
    \textbf{Process:} Conduct a rigorous due diligence process to uncover any hidden liabilities or issues. \\
    \textbf{Objectivity:} Engage independent third parties to ensure unbiased evaluations.
    
    \item \textbf{Swap Ratio and Financing} \\
    \textbf{Swap Ratio:} Determine an appropriate swap ratio if stock is part of the transaction. \\
    \textbf{Financing Options:} Evaluate whether to use cash, stock, or debt to finance the deal based on the company’s strategic and financial goals.
\end{enumerate}


\paragraph{Pathway Integration}
Pathway stands at a unique position to implement the Connectors and ETL for IB Automation, for market information is highly dynamic and must be analyzed in real time in order to form quick decisions. If Real Time market sentiments are not taken into consideration while taking decisions, the workflow of various Mergers and Acquisitions and IPOs can be delayed. The Dynamic nature of Pathway Document Store and it's capability to integrate streaming services makes Pathway the best fit for IB applications.

%\paragraph{Comparison}

%\paragraph{Business Impact}


\begin{figure*}[ht]
    \centering
    \includegraphics[width=\linewidth]{Graphics/IB_SOM.png}
    \caption{TAM-SAM-SOM Analysis for IBs}
    \label{fig:enter-label}
\end{figure*}

\subsection{Law Firms} 
\subsection{Arbitration}
\subsection{Generalized Applications}